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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
| Topic 2: Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Topic 3: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 4: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 5: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources |
| Topic 6: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs |
| Topic 7: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics |
| Topic 8: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code |
| Topic 9: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Implement schema evolution and management - Apply data cleansing and validation rules |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
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NEW QUESTION # 229
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
Answer: C
Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.
NEW QUESTION # 230
A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
Answer: A
Explanation:
The CAN VIEW permission on a cluster allows users to see cluster details, including driver and executor logs. This is the minimal permission required for the development team to access logs without granting them the ability to modify, restart, or attach notebooks to the cluster.
NEW QUESTION # 231
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?
Answer: B
Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.
NEW QUESTION # 232
The view updates represents an incremental batch of all newly ingested data to be inserted or Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
Answer: B
Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
NEW QUESTION # 233
A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)
Answer: A,D
Explanation:
Databricks Jobs supports defining multi-task workflows that include notebooks, SQL statements, and Python wheel tasks. These can be configured with retry policies, dependency chains, and failure notifications. The correct practice, as stated in the documentation, is to use the Jobs REST API (/jobs/create) or Databricks Asset Bundles to define multi-task jobs, and then trigger them programmatically using /jobs/run-now, CLI, or SDK. This allows the team to maintain full job history, handle retries automatically, and receive alerts via configured email notifications. Using
/jobs/runs/submit creates one-off ad hoc runs without maintaining dependency visibility.
Therefore, options B and C together satisfy the operational, automation, and governance requirements.
NEW QUESTION # 234
......
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