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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Alerting | 10% | - Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging |
| Topic 2: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Compliance standards implementation - Access control and permissions |
| Topic 4: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Topic 5: Data Modelling | 6% | - Delta Lake table design - Schema design and management - Medallion Architecture implementation |
| Topic 6: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Data validation and quality checks - Handling missing or inconsistent data |
| Topic 7: Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Topic 8: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Topic 9: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 10: Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs |
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NEW QUESTION # 30
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance.
How should the data engineer create the patient_encounters table?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog. Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.
NEW QUESTION # 31
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data . They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline.
Which configuration should the team use when writing data to the Delta table?
Answer: D
Explanation:
When writing data to Delta Lake tables, the option mergeSchema = true allows automatic evolution of the table schema by merging new columns or data types introduced in the incoming data with the existing table definition. This ensures pipelines remain resilient to upstream schema changes, especially in append-only or streaming ingestion scenarios. The Databricks documentation specifies that this option should be enabled for schema-on-write flexibility, while overwriteSchema replaces the existing schema entirely and ignoreChanges applies only to CDC operations. validateSchema ensures consistency and does not handle evolution.
Therefore, B (mergeSchema = true) is the correct configuration for handling automatic schema evolution safely.
NEW QUESTION # 32
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?
Answer: A
NEW QUESTION # 33
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
Answer: C
Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.
NEW QUESTION # 34
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?
Answer: D
Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
Reference:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table
NEW QUESTION # 35
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