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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling and Storage | 20% | - File Formats - Data Modeling - Storage Optimization |
| Topic 2: Data Quality and Governance | 12% | - Data Quality - Data Lineage - Governance |
| Topic 3: Data Processing | 28% | - ETL Pipelines - Spark SQL - Structured Streaming - Data Transformation |
| Topic 4: Monitoring and Troubleshooting | 16% | - Performance Optimization - Monitoring - Troubleshooting |
| Topic 5: Databricks Lakehouse Platform | 24% | - Lakehouse Architecture - Unity Catalog - Delta Lake - Data Management |
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?
- A. Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
- B. Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
- C. Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
- D. Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
Correct Answer: D 🗳️
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The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
- A. Can edit
- B. Can manage
- C. Can Read
- D. Can run
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?
- A. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
- B. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
- C. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
- D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
- E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
Correct Answer: C 🗳️
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The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:
What does the data governance team need to do next to achieve this goal?
- A. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask); - B. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask; - C. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup'); - D. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
Correct Answer: B 🗳️
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A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?
- A. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
- B. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
- C. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
- D. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
Correct Answer: C 🗳️
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