有効的なDatabricks-Certified-Data-Engineer-Professional最新受験攻略一回合格-信頼的なDatabricks-Certified-Data-Engineer-Professional模擬試験問題集

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Topic 2: Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit
Topic 3: Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices
Topic 4: Data Modelling	6%	- Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning
Topic 5: Monitoring and Alerting	10%	- Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics
Topic 6: Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Topic 7: Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic
Topic 8: Data Transformation, Cleansing, and Quality	10%	- Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management
Topic 9: Cost & Performance Optimisation	13%	- Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance
Topic 10: Data Governance	7%	- Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q183-Q188):

質問 # 183
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Deduplicate records in each batch by pk_id and overwrite the target table.
B. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.

正解：C

質問 # 184
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A. PII masking is only required for direct identifiers.
B. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
C. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
D. Dynamic data masking is applied only at rest, so it does not affect query performance.

正解：B

解説：
Consistent PII masking in distributed batch and streaming pipelines requires centrally defined and governed masking logic. Standardizing masking functions and enforcing them through Unity Catalog ensures that the same rules are applied uniformly across all datasets and execution paths, preventing inconsistencies across nodes and workloads.

質問 # 185
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
C. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
D. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

正解：C

解説：
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.

質問 # 186
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. Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
B. Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_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 dropDuplicates() to remove out-of-order and duplicate records in LDP.

正解：B

解説：
When handling CDC data, sequencing is critical because updates may arrive out of order or multiple changes may occur for the same record at the same timestamp. Databricks' AUTO CDC APIs provide built-in constructs to handle ordering logic.
The correct mechanism is to use the SEQUENCE BY clause in the CDC configuration.
Specifically, when both update_timestamp and update_sequence_id exist, the recommended approach is:
SEQUENCE BY STRUCT(event_timestamp, update_sequence_id)
This ensures that within the same record key, the engine applies updates in the exact sequence they occurred, resolving conflicts where multiple updates share the same timestamp but differ in sequence ID.
Option A (track_history_column_list) is used for historical tracking and auditing changes, not for sequencing logic. It ensures lineage but does not enforce correct event order.
Option B (dropDuplicates()) only removes exact duplicates; it cannot guarantee sequencing correctness when multiple updates exist.
Option C is correct: SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) explicitly enforces ordering, as recommended by the CDC pipeline guidelines.
Option D (window function) would be a manual approach in Spark Structured Streaming, but Lakeflow Declarative Pipelines already provide native CDC sequencing support, making this unnecessary.
Thus, the best practice per Databricks CDC documentation is to use Option C with SEQUENCE BY STRUCT.

質問 # 187
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Executor's detail screen and Executor's log files
B. Driver's and Executor's log files
C. Query's detail screen and Job's detail screen
D. Stage's detail screen and Query's detail screen
E. Stage's detail screen and Executor's log files

正解：E

解説：
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.

質問 # 188
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