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| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.
In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.
The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?
Correct Answer: D 🗳️
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A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?
Correct Answer: D 🗳️
Explanation: Only visible for Exam4PDF members. You can sign-up / login (it's free).
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?
Correct Answer: B 🗳️
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?
Correct Answer: C 🗳️
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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.)
Correct Answer: A,D 🗳️
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