Pass Amazon MLS-C01 PDF Dumps Recently Updated 324 Questions [Q128-Q153]

Pass Amazon MLS-C01 PDF Dumps | Recently Updated 324 Questions

Updated Test Engine to Practice MLS-C01 Dumps & Practice Exam

Amazon AWS-Certified-Machine-Learning-Specialty exam is an excellent certification program for professionals who want to build a career in machine learning or data science. It provides a comprehensive and in-depth understanding of the AWS platform and its machine learning services, and it validates an individual's knowledge and skills in designing, building, and deploying machine learning solutions on AWS.

Achieving the AWS Certified Machine Learning - Specialty certification through the Amazon MLS-C01 exam can demonstrate to employers and clients that you have the skills and knowledge needed to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification can help individuals advance their careers as data scientists, machine learning engineers, and solution architects.

 

NEW QUESTION # 128
A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

• A. Apply principal component analysis (PCA).
• B. Apply one-hot encoding category-based variables.
• C. Remove a portion of highly correlated features from the dataset.
• D. Use a linear-based algorithm to train the model.
• E. Apply min-max feature scaling to the dataset.

Answer: A,C

Explanation:
B: Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.
C: Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other.
This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: = Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.
Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.

NEW QUESTION # 129
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent Why could this be an issue for the linear least squares regression model?

• A. It could cause the backpropagation algorithm to fail during training
• B. It could modify the loss function during optimization causing it to fail during training
• C. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
• D. It could create a singular matrix during optimization which fails to define a unique solution

Answer: B

NEW QUESTION # 130
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• C. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• D. Train a custom XGBoost model to forecast demand for the new product

Answer: C

Explanation:
Explanation
The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.
One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series.
DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.
DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.
Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.
References:
DeepAR Forecasting Algorithm - Amazon SageMaker
Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting

NEW QUESTION # 131
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

• A. One-hot encoding
• B. Normalization transformation
• C. Tokenization
• D. Binarization

Answer: A

Explanation:
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
* One-Hot Encoding - Amazon SageMaker
* One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
* One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 132
A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?

• A. Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.
• B. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
• C. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
• D. Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

Answer: D

NEW QUESTION # 133
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

• A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
• B. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
• C. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
• D. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

Answer: A

NEW QUESTION # 134
A Data Scientist needs to analyze employment dat
a. The dataset contains approximately 10 million
observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

• A. Cross-validation
• B. High-degree polynomial transformation
• C. One hot encoding
• D. Logarithmic transformation
• E. Numerical value binning

Answer: D,E

Explanation:
To fix the incorrectly skewed data, the Data Scientist can apply two feature transformations: numerical value binning and logarithmic transformation. Numerical value binning is a technique that groups continuous values into discrete bins or categories. This can help reduce the skewness of the data by creating more balanced frequency distributions. Logarithmic transformation is a technique that applies the natural logarithm function to each value in the data. This can help reduce the right skewness of the data by compressing the large values and expanding the small values. Both of these transformations can make the data more suitable for machine learning algorithms that assume normality of the data. References:
Data Transformation - Amazon SageMaker
Transforming Skewed Data for Machine Learning

NEW QUESTION # 135
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

• A. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
• B. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
• C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
• D. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: C

Explanation:
Explanation
A precision-recall curve is a plot that shows the trade-off between the precision and recall of a binary classifier as the decision threshold is varied. It is a useful tool for evaluating and comparing the performance of different models. To generate a precision-recall curve, the following steps are needed:
Calculate the precision and recall values for different threshold values using the predictions and the true labels of the data.
Plot the precision values on the y-axis and the recall values on the x-axis for each threshold value.
Optionally, calculate the area under the curve (AUC) as a summary metric of the model performance.
Among the four options, option C requires the least coding effort to generate and share a visualization of the daily precision-recall curve from the predictions. This option involves the following steps:
Run a daily Amazon EMR workflow to generate precision-recall data: Amazon EMR is a service that allows running big data frameworks, such as Apache Spark, on a managed cluster of EC2 instances.
Amazon EMR can handle large-scale data processing and analysis, such as calculating the precision and recall values for different threshold values from 100 TB of predictions. Amazon EMR supports various languages, such as Python, Scala, and R, for writing the code to perform the calculations. Amazon EMR also supports scheduling workflows using Apache Airflow or AWS Step Functions, which can automate the daily execution of the code.
Save the results in Amazon S3: Amazon S3 is a service that provides scalable, durable, and secure object storage. Amazon S3 can store the precision-recall data generated by Amazon EMR in a cost-effective and accessible way. Amazon S3 supports various data formats, such as CSV, JSON, or Parquet, for storing the data. Amazon S3 also integrates with other AWS services, such as Amazon QuickSight, for further processing and visualization of the data.
Visualize the arrays in Amazon QuickSight: Amazon QuickSight is a service that provides fast, easy-to-use, and interactive business intelligence and data visualization. Amazon QuickSight can connect to Amazon S3 as a data source and import the precision-recall data into a dataset. Amazon QuickSight can then create a line chart to plot the precision-recall curve from the dataset. Amazon QuickSight also supports calculating the AUC and adding it as an annotation to the chart.
Publish them in a dashboard shared with the Business team: Amazon QuickSight allows creating and publishing dashboards that contain one or more visualizations from the datasets. Amazon QuickSight also allows sharing the dashboards with other users or groups within the same AWS account or across different AWS accounts. The Business team can access the dashboard with read-only permissions and view the daily precision-recall curve from the predictions.
The other options require more coding effort than option C for the following reasons:
Option A: This option requires writing code to plot the precision-recall curve from the data stored in Amazon S3, as well as creating a mechanism to share the plot with the Business team. This can involve using additional libraries or tools, such as matplotlib, seaborn, or plotly, for creating the plot, and using email, web, or cloud services, such as AWS Lambda or Amazon SNS, for sharing the plot.
Option B: This option requires transforming the predictions into a format that Amazon QuickSight can recognize and import as a data source, such as CSV, JSON, or Parquet. This can involve writing code to process and convert the predictions, as well as uploading them to a storage service, such as Amazon S3 or Amazon Redshift, that Amazon QuickSight can connect to.
Option D: This option requires writing code to generate precision-recall data in Amazon ES, as well as creating a dashboard to visualize the data. Amazon ES is a service that provides a fully managed Elasticsearch cluster, which is mainly used for search and analytics purposes. Amazon ES is not designed for generating precision-recall data, and it requires using a specific data format, such as JSON, for storing the data. Amazon ES also requires using a tool, such as Kibana, for creating and sharing the dashboard, which can involve additional configuration and customization steps.
References:
Precision-Recall
What Is Amazon EMR?
What Is Amazon S3?
[What Is Amazon QuickSight?]
[What Is Amazon Elasticsearch Service?]

NEW QUESTION # 136
A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.
The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.
Which approach will maximize transcription accuracy during the development phase?

• A. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.
• B. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.
• C. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
• D. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.

Answer: C

Explanation:
The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
The other options are not as effective as option C for the following reasons:
* Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.
* Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.
* Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.
Amazon Transcribe - Custom Vocabulary
Amazon Transcribe - Custom Language Models
[Amazon Lex - Limits]

NEW QUESTION # 137
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

• A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
• B. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
• C. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
• D. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.

Answer: C

Explanation:
Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.
Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.
Bring your own model with Amazon SageMaker script mode
GitHub - aws-samples/amazon-sagemaker-script-mode
Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

NEW QUESTION # 138
A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.
Which TensorFlow estimator configuration will train the model MOST cost-effectively?

• A. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
• B. Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
• C. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.
• D. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer: C

Explanation:
The TensorFlow estimator configuration that will train the model most cost-effectively is to turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter, turn on managed spot training by setting the use_spot_instances parameter to True, and pass the script to the estimator in the call to the TensorFlow fit() method. This configuration will optimize the model for the target hardware platform, reduce the training cost by using Amazon EC2 Spot Instances, and use the custom Python script without any modification.
SageMaker Training Compiler is a feature of Amazon SageMaker that enables you to optimize your TensorFlow, PyTorch, and MXNet models for inference on a variety of target hardware platforms.
SageMaker Training Compiler can improve the inference performance and reduce the inference cost of your models by applying various compilation techniques, such as operator fusion, quantization, pruning, and graph optimization. You can enable SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter to the TensorFlow estimator constructor1.
Managed spot training is another feature of Amazon SageMaker that enables you to use Amazon EC2 Spot Instances for training your machine learning models. Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS Cloud. Spot Instances are available at up to a 90% discount compared to On- Demand prices. You can use Spot Instances for various fault-tolerant and flexible applications. You can enable managed spot training by setting the use_spot_instances parameter to True and specifying the max_wait and max_run parameters in the TensorFlow estimator constructor2.
The TensorFlow estimator is a class in the SageMaker Python SDK that allows you to train and deploy TensorFlow models on SageMaker. You can use the TensorFlow estimator to run your own Python script on SageMaker, without any modification. You can pass the script to the estimator in the call to the TensorFlow fit() method, along with the location of your input data. The fit() method starts a SageMaker training job and runs your script as the entry point in the training containers3.
The other options are either less cost-effective or more complex to implement. Adjusting the training script to use distributed data parallelism would require modifying the script and specifying appropriate values for the distribution parameter, which could increase the development time and complexity. Setting the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter would not reduce the cost, as it would only specify the maximum duration of the training job, regardless of the instance type.
References:
* 1: Optimize TensorFlow, PyTorch, and MXNet models for deployment using Amazon SageMaker Training Compiler | AWS Machine Learning Blog
* 2: Managed Spot Training: Save Up to 90% On Your Amazon SageMaker Training Jobs | AWS Machine Learning Blog
* 3: sagemaker.tensorflow - sagemaker 2.66.0 documentation

NEW QUESTION # 139
A social media company wants to develop a machine learning (ML) model to detect Inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training.
...company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folder ...ain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same sue and generates two input manifest files named training.1st and validation.1st, for the ..ing dataset and the validation dataset. respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset.
...h additional data preparation steps should the company take before uploading the files to Amazon S3?

• A. Compress the training and validation directories by using the Snappy compression library Upload the manifest and compressed files to the training S3 bucket
• B. Generate two RecordIO files, training rec and validation.rec. from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordlO files to the training S3 bucket.
• C. Compress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.
• D. Generate two Apache Parquet files, training.parquet and validation.parquet. by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket

Answer: B

Explanation:
Explanation
The SageMaker image classification algorithm supports both RecordIO and image content types for training in file mode, and supports the RecordIO content type for training in pipe mode1. However, the algorithm also supports training in pipe mode using the image files without creating RecordIO files, by using the augmented manifest format2. In this case, the company should generate

NEW QUESTION # 140
A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance.
The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

• A. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
• B. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
• C. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.
• D. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

Answer: A

Explanation:
Explanation
The best approach to address all of the requirements with the least development effort is to create an AWS Glue job, convert the scripts to PySpark, execute the pipeline, and store the results in Amazon S3. This is because:
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics 1. AWS Glue can run Python and Scala scripts to process data from various sources, such as Amazon S3, Amazon DynamoDB, Amazon Redshift, and more 2. AWS Glue also provides a serverless Apache Spark environment to run ETL jobs, eliminating the need to provision and manage servers 3.
PySpark is the Python API for Apache Spark, a unified analytics engine for large-scale data processing 4. PySpark can perform various data transformations and manipulations on structured and unstructured data, such as cleaning, enriching, and compressing 5. PySpark can also leverage the distributed computing power of Spark to handle terabytes of data efficiently and scalably 6.
By creating an AWS Glue job and converting the scripts to PySpark, the company can move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers. The company can also reduce the development effort by using the AWS Glue console, AWS SDK, or AWS CLI to create and run the job 7. Moreover, the company can use the AWS Glue Data Catalog to store and manage the metadata of the data sources and targets 8.
The other options are not as suitable as option C for the following reasons:
Option A is not optimal because loading the data into an Amazon Redshift cluster and executing the pipeline by using SQL will incur additional costs and complexity for the company. Amazon Redshift is a fully managed data warehouse service that enables fast and scalable analysis of structured data .
However, it is not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, using SQL to perform these tasks may not be as expressive and flexible as using Python scripts. Furthermore, the company will have to provision and configure the Amazon Redshift cluster, and load and unload the data from Amazon S3, which will increase the development effort and time.
Option B is not feasible because loading the data into Amazon DynamoDB and converting the scripts to an AWS Lambda function will not work for the company's use case. Amazon DynamoDB is a fully managed key-value and document database service that provides fast and consistent performance at any scale . However, it is not suitable for storing and processing terabytes of data daily, as it has limits on the size and throughput of each table and item . Moreover, using AWS Lambda to execute the pipeline will not be efficient or cost-effective, as Lambda has limits on the memory, CPU, and execution time of each function . Therefore, using Amazon DynamoDB and AWS Lambda will not meet the company's requirements for processing large amounts of data quickly and reliably.
Option D is not relevant because creating a set of individual AWS Lambda functions to execute each of the scripts and building a step function by using the AWS Step Functions Data Science SDK will not address the main issue of moving the scripts out of Amazon EC2. AWS Step Functions is a fully managed service that lets you coordinate multiple AWS services into serverless workflows . The AWS Step Functions Data Science SDK is an open source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions . However, these services and tools are not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, as mentioned in option B, using AWS Lambda to execute the scripts will not be efficient or cost-effective for the company's use case.
References:
What Is AWS Glue?
AWS Glue Components
AWS Glue Serverless Spark ETL
PySpark - Overview
PySpark - RDD
PySpark - SparkContext
Adding Jobs in AWS Glue
Populating the AWS Glue Data Catalog
[What Is Amazon Redshift?]
[What Is Amazon DynamoDB?]
[Service, Account, and Table Quotas in DynamoDB]
[AWS Lambda quotas]
[What Is AWS Step Functions?]
[AWS Step Functions Data Science SDK for Python]

NEW QUESTION # 141
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the number of epochs for model training.
• C. Increase the dropout rate for the second-to-last layer.
• D. Increase the number of layers for the neural network.

Answer: A

Explanation:
To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.
References:
1: Image Augmentation - Amazon SageMaker

NEW QUESTION # 142
A company is interested in building a fraud detection model. Currently, the Data Scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?

• A. Oversampling using SMOTE
• B. Undersampling
• C. Oversampling using bootstrapping
• D. Class weight adjustment

Answer: A

Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting informatio

NEW QUESTION # 143
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 0
• B. 1
• C. 2
• D. 3

Answer: B

Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
* Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
* K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

NEW QUESTION # 144
A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.
Which solution will meet these requirements with the LEAST development effort?

• A. Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
• B. Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.
• C. Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
• D. Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.

Answer: A

Explanation:
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability - Amazon SageMaker Clarify - AWS Amazon SageMaker Clarify Model Explainability Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability GitHub - aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning

NEW QUESTION # 145
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• C. Train a custom XGBoost model to forecast demand for the new product
• D. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Answer: A

NEW QUESTION # 146
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)

• A. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.
• B. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.
• C. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
• D. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.
• E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

Answer: A,B

Explanation:
To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:
Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.
Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.
The other options are not valid because:
Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.
Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.
Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:
Amazon Translate
Amazon Comprehend
Amazon SageMaker
Amazon SageMaker Neural Topic Model (NTM) Algorithm
Amazon SageMaker BlazingText
Amazon SageMaker Seq2Seq
Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm
Amazon Lex

NEW QUESTION # 147
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

• A. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
• B. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
• D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Answer: D

Explanation:
Explanation
The Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm is a supervised learning algorithm that can perform both classification and regression tasks. It can also handle time series data, such as the air quality data in this case. The kNN algorithm works by finding the k most similar instances in the training data to a given query instance, and then predicting the output based on the average or majority of the outputs of the k nearest neighbors. The kNN algorithm can be configured to use different distance metrics, such as Euclidean or cosine, to measure the similarity between instances. To use the kNN algorithm on the single time series consisting of the full year of data, the Machine Learning Specialist needs to set the predictor_type parameter to regressor, as the output variable (air quality in parts per million of contaminates) is a continuous value. The kNN algorithm can then forecast the air quality for the next 2 days by finding the k most similar days in the past year and averaging their air quality values.
References:
Amazon SageMaker k-Nearest-Neighbors (kNN) Algorithm - Amazon SageMaker Time Series Forecasting using k-Nearest Neighbors (kNN) in Python | by ...
Time Series Forecasting with k-Nearest Neighbors | by Nishant Malik ...

NEW QUESTION # 148
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

• A. One-hot encoding
• B. Normalization transformation
• C. Tokenization
• D. Binarization

Answer: A

Explanation:
Explanation
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
One-Hot Encoding - Amazon SageMaker
One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 149
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

• A. Use gradient checking in the model
• B. Make the neural network architecture complex.
• C. Add vanishing gradient to the model
• D. Perform data augmentation on the training data
• E. Add L2 regularization to the model

Answer: D,E

Explanation:
The issue described in the question is a sign of overfitting, which is a common problem in machine learning when the model learns the noise and details of the training data too well and fails to generalize to new and unseen data. Overfitting can result in a low training error rate but a high test error rate, which indicates poor performance and validity of the model. There are several techniques that can be used to prevent or reduce overfitting, such as data augmentation and regularization.
Data augmentation is a technique that applies various transformations to the original training data, such as rotation, scaling, cropping, flipping, adding noise, changing brightness, etc., to create new and diverse data samples. Data augmentation can increase the size and diversity of the training data, which can help the model learn more features and patterns and reduce the variance of the model. Data augmentation is especially useful for image data, as it can simulate different scenarios and perspectives that the model may encounter in real life. For example, in the question, the device uses a camera to observe drivers' behavior, so data augmentation can help the model deal with different lighting conditions, angles, distances, etc. Data augmentation can be done using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, OpenCV, etc12 Regularization is a technique that adds a penalty term to the model's objective function, which is typically based on the model's parameters. Regularization can reduce the complexity and flexibility of the model, which can prevent overfitting by avoiding learning the noise and details of the training data. Regularization can also improve the stability and robustness of the model, as it can reduce the sensitivity of the model to small fluctuations in the data. There are different types of regularization, such as L1, L2, dropout, etc., but they all have the same goal of reducing overfitting. L2 regularization, also known as weight decay or ridge regression, is one of the most common and effective regularization techniques. L2 regularization adds the squared norm of the model's parameters multiplied by a regularization parameter (lambda) to the model's objective function. L2 regularization can shrink the model's parameters towards zero, which can reduce the variance of the model and improve the generalization ability of the model. L2 regularization can be implemented using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, Scikit-learn, etc34 The other options are not valid or relevant for resolving the issue of overfitting. Adding vanishing gradient to the model is not a technique, but a problem that occurs when the gradient of the model's objective function becomes very small and the model stops learning. Making the neural network architecture complex is not a solution, but a possible cause of overfitting, as a complex model can have more parameters and more flexibility to fit the training data too well. Using gradient checking in the model is not a technique, but a debugging method that verifies the correctness of the gradient computation in the model. Gradient checking is not related to overfitting, but to the implementation of the model.

NEW QUESTION # 150
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

• A. indicate a copy of the samples in the test database in the training dataset
• B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• C. Add more deep trees to the random forest to enable the model to learn more features.
• D. Change the cost function so that false negatives have a higher impact on the cost value than false positives
• E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: B,D

Explanation:
Explanation
The Data Science team is facing a problem of imbalanced data, where the positive class (paid users) is much less frequent than the negative class (non-paid users). This can cause the random forest model to be biased towards the majority class and have poor performance on the minority class. To mitigate this issue, the Data Science team can try the following approaches:
C: Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. This is a technique called data augmentation, which can help increase the size and diversity of the training data for the minority class. This can help the random forest model learn more features and patterns from the positive class and reduce the imbalance ratio.
D: Change the cost function so that false negatives have a higher impact on the cost value than false positives. This is a technique called cost-sensitive learning, which can assign different weights or costs to different classes or errors. By assigning a higher cost to false negatives (predicting non-paid when the user is actually paid), the random forest model can be more sensitive to the minority class and try to minimize the misclassification of the positive class.
References:
Bagging and Random Forest for Imbalanced Classification
Surviving in a Random Forest with Imbalanced Datasets
machine learning - random forest for imbalanced data? - Cross Validated Biased Random Forest For Dealing With the Class Imbalance Problem

NEW QUESTION # 151
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Choose three.)

• A. Decrease regularization.
• B. Decrease dropout.
• C. Increase feature combinations.
• D. Decrease feature combinations.
• E. Increase regularization.
• F. Increase dropout.

Answer: B,C,E

NEW QUESTION # 152
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

• A. Use a regression algorithm to run predictions.
• B. Use a forecasting algorithm to run predictions.
• C. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
  "enrolled" or "not enrolled."
• D. Use a classification algorithm to run predictions
• E. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."

Answer: D,E

Explanation:
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named
"enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
Use Amazon SageMaker Ground Truth to Label Data
Classification Algorithm in Machine Learning

NEW QUESTION # 153
......

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