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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Topic 2: Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
| Topic 3: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 4: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Implement schema evolution and management - Apply data cleansing and validation rules |
| Topic 5: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Topic 6: Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Topic 7: Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code |
| Topic 8: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Topic 9: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Topic 10: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
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NEW QUESTION # 199
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?
Answer: D
Explanation:
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NEW QUESTION # 200
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
Answer: A
Explanation:
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.
NEW QUESTION # 201
A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?
Answer: C
Explanation:
This window specification partitions the data by customer_id, orders transactions by transaction_timestamp, and defines the frame from the first transaction through the current one.
This guarantees that the cumulative sum is computed independently per customer and strictly follows the temporal order, including all prior transactions up to the current row.
NEW QUESTION # 202
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?
Answer: C
Explanation:
Unity Catalog supports programmatic tagging through the ALTER TABLE statement. Using SET TAGS allows tags to be added or updated directly on a table as part of automated ETL workflows, making it the correct and supported SQL command for managing table tags.
NEW QUESTION # 203
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
Answer: D
Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.
NEW QUESTION # 204
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