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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Topic 2: Developing Code for Data Processing using Python and SQL | ~22% | - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs |
| Topic 3: Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Topic 4: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 5: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 6: Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Topic 7: Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| Topic 8: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 9: Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
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NEW QUESTION # 48
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?
Answer: B
Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.
NEW QUESTION # 49
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?
Answer: C
Explanation:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.
NEW QUESTION # 50
A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader.
The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files. Which Auto Loader configuration should the data engineer used to achieve this?
Answer: B
Explanation:
cloudFiles.maxBytesPerTrigger limits the total volume of data scanned in each micro-batch based on size rather than file count. Setting it to 1 GB ensures predictable ingestion throughput even when large files arrive, preventing any single trigger from overwhelming the streaming job.
NEW QUESTION # 51
A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?
Answer: A
Explanation:
To retrieve information about the latest job runs along with their repair history, you use the jobs/runs/list endpoint with the job_id and include_history=true. This endpoint returns a list of runs for a specific job, including details about retries and repair attempts, which is not available via runs/get that retrieves a single run by run_id.
NEW QUESTION # 52
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 auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. 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?
Answer: C
Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.
NEW QUESTION # 53
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