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| Section | Weight | Objectives |
|---|---|---|
| Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs |
| Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources |
Question 1
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?
A. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
B. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
C. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
D. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
Question 2
A view is registered with the following code:
Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?
A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
Question 3
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
A. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
B. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
C. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
D. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
E. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
Question 4
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?
A. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
B. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
C. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
D. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
Question 5
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?
A. SHOW TBLPROPERTIES dev.pii test
B. SHOW TABLES dev
C. DESCRIBE EXTENDED dev.pii test
D. DESCRIBE DETAIL dev.pii test
E. DESCRIBE HISTORY dev.pii test
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: E | Question 4 Answer: B | Question 5 Answer: C |
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