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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
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NEW QUESTION # 62
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:
The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?
Answer: C
NEW QUESTION # 63
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
Answer: D
Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.
NEW QUESTION # 64
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?
Answer: B
Explanation:
This query correctly aggregates usage at a daily granularity by truncating the usage start timestamp to the day and summing the usage quantity, which represents DBUs. Grouping by both the derived daily value and the SKU name ensures usage is accurately categorized by product tier across all workspaces.
NEW QUESTION # 65
A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?
Answer: B
Explanation:
The recommended way to troubleshoot and collect detailed job logs is through the Job Run Details page in Databricks. From there, engineers can export run logs or configure automatic log delivery to a storage destination. The driver and event logs available under compute details provide stdout, stderr, and cluster lifecycle context required for root-cause analysis.
NEW QUESTION # 66
Which distribution does Databricks support for installing custom Python code packages?
Answer: D
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in-workflows
NEW QUESTION # 67
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