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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| Topic 3: Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Topic 4: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 5: Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Topic 6: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 7: Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Topic 8: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 9: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
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NEW QUESTION # 73
Which Python variable contains a list of directories to be searched when trying to locate required modules?
Answer: B
Explanation:
sys. path is a built-in variable within the sys module. It contains a list of directories that the interpreter will search in for the required module.
NEW QUESTION # 74
A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?
Answer: C
Explanation:
According to Databricks' documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables--it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks. Only Databricks-to- Databricks sharing supports dynamic data sharing with update visibility and streaming reads.
Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.
NEW QUESTION # 75
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?
Answer: B
Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.
NEW QUESTION # 76
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: A
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 # 77
Which method can be used to determine the total wall-clock time it took to execute a query?
Answer: C
Explanation:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs.
Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.
NEW QUESTION # 78
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