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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Assessing and communicating business impact
- Managing incorrect results
- Defining outcome of model predictions
- Defining business problems
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining output use
- Defining the input (features) and predicted output format
- Define ML problem
- Define business success criteria
- Success metrics
- Identifying data sources
- Identifying nonML solutions
- Determination of when a model is deemed unsuccessful
- Assessing data readiness
- Assessing ML solution readiness
- Key results
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Defining problem type (classification, regression, clustering, etc.)
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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- Step 1: Go to the Google Official Site
- Step 2: Read the instruction carefully
- Step 3: Follow the given steps
- Step 4: Apply for the Professional Machine Learning Engineer Exam
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Designing ML solutions | - Framing ML problems
|
| Topic 2: Data preparation and processing | - Data ingestion and pipelines
|
| Topic 3: Deployment and operations | - Monitoring and maintenance
|
| Topic 4: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 5: ML model development | - Model training and tuning
|
Google Professional Machine Learning Engineer Sample Questions:
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?
- A. Apply one-hot encoding on the categorical variables in the test data
- B. Collect more data representing all categories
- C. Randomly redistribute the data, with 70% for the training set and 30% for the test set
- D. Use sparse representation in the test set.
Correct Answer: A 🗳️
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You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
- A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
- B. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
- C. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
- D. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
Correct Answer: B 🗳️
You deployed a conversational application that uses a large language model (LLM). The application has 1,000 users. You collect user feedback about the verbosity and accuracy of the model 's responses. The user feedback indicates that the responses are factually correct but users want different levels of verbosity depending on the type of question. You want the model to return responses that are more consistent with users' expectations, and you want to use a scalable solution. What should you do?
- A. Experiment with other proprietary and open-source LLMs. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results.
- B. Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application 's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts.
- C. Implement a keyword-based routing layer. If the user's input contains the words "detailed" or
"description," return a verbose response. If the user's input contains the word "fact." re-prompt the language model to summarize the response and return a concise response. - D. Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine tuning of the foundational model.
Re-evaluate the verbosity of responses with the tuned model.
Correct Answer: B 🗳️
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You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?
- A. Generate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
- B. Create a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
- C. Create a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.
- D. Train another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.
Correct Answer: B 🗳️
You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery.
You want to minimize the serving latency as much as possible. What should you do?
- A. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
- B. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
- C. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
- D. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
Correct Answer: C 🗳️
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No help, Full refund!
Actual4Exams confidently stands behind all its offerings by giving Unconditional "No help, Full refund" Guarantee. Since the time our operations started we have never seen people report failure in the Google Professional-Machine-Learning-Engineer exam after using our products. With this feedback we can assure you of the benefits that you will get from our products and the high probability of clearing the Professional-Machine-Learning-Engineer exam.
We still understand the effort, time, and money you will invest in preparing for your certification exam, which makes failure in the Google Professional-Machine-Learning-Engineer exam really painful and disappointing. Although we cannot reduce your pain and disappointment but we can certainly share with you the financial loss.
This means that if due to any reason you are not able to pass the Professional-Machine-Learning-Engineer actual exam even after using our product, we will reimburse the full amount you spent on our products. you just need to mail us your score report along with your account information to address listed below within 7 days after your unqualified certificate came out.
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