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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Ensuring Data Security and Compliance | - Data Security
|
| Topic 2: Data Sharing and Federation | - Delta Sharing
|
| Topic 3: Monitoring and Alerting | - Monitoring
|
| Topic 4: Data Governance | - Unity Catalog Permissions
|
| Topic 5: Debugging and Deploying | - Debugging and Troubleshooting
|
| Topic 6: Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
|
| Topic 7: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 8: Data Transformation, Cleansing, and Quality | - Data Quality
|
| Topic 9: Data Modelling | - Scalable Data Models
|
| Topic 10: Cost & Performance Optimisation | - Delta Optimization
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F
Which anonymised code should be used to achieve the required outcome?
- A. df.withColumn ("user_email", F.sha2 ("user_email"))
- B. df.withColumn ("user_emai", F.expr("uuid()"))
- C. df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
- D. df.withColumn ("hashed_email", sha2 ("user_email"))
Correct Answer: A 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
- A. Spill resulting from attached volume storage being too small.
- B. Task queueing resulting from improper thread pool assignment.
- C. Skew caused by more data being assigned to a subset of spark-partitions.
- D. Credential validation errors while pulling data from an external system.
- E. Network latency due to some cluster nodes being in different regions from the source data
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
- A. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
- B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
- C. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
- D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?
- A. Reference each expectation with @dlt.expect decorators in the table declaration.
- B. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
- C. Invoke an external API to validate records against the metadata rules.
- D. Use a SQL CONSTRAINT block referencing the JSON file path.
Correct Answer: B 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?
- A. Define and import unit test functions from a separate Databricks notebook
- B. Run unit tests against non-production data that closely mirrors production
- C. Define and unit test functions using Files in Repos
- D. Define units test and functions within the same notebook
Correct Answer: B 🗳️
Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).
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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 Databricks Certified-Data-Engineer-Professional 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 Certified-Data-Engineer-Professional exam.
We still understand the effort, time, and money you will invest in preparing for your certification exam, which makes failure in the Databricks Certified-Data-Engineer-Professional 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 Certified-Data-Engineer-Professional 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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