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| Section | Weight | Objectives |
|---|---|---|
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Data encryption and masking - Compliance standards implementation |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Monitoring and Alerting | 10% | - Setting up alerts and notifications - Pipeline observability and logging - Performance and health monitoring |
| Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
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NEW QUESTION # 170
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.
NEW QUESTION # 171
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?
Answer: D
Explanation:
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active.
Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html
NEW QUESTION # 172
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team.
The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?
Answer: A
Explanation:
Delta Lake introducedDelta Universal Format (Delta UniForm), which allowsseamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can beconverted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
* (A) Require the analytics team to use a tool that supports Delta table
* Incorrect:While Delta Lake is widely used, requiring the team to change toolsis not a flexible or scalable solution.
* (B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table
* Incorrect:
* The uniform featuremust be enabled after conversion.
* You cannotdirectly enable uniformwithout firstconverting the table.
* (C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team
* Incorrect:
* Creating a separate Iceberg copy wouldduplicate storage and increase maintenance complexity.
* This is not necessary whenDelta UniForm allows direct compatibility with Iceberg.
* (D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table
* Correct:
* The best approach is toconvert the existing Delta table to Icebergusing the Databricks Delta to Iceberg migration tools.
* After conversion,enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice forinteroperability between Delta and Icebergis toconvert the Delta table to Iceberg and enable uniform, ensuringcross-compatibilitywithout data duplication.Thus,Option (D) is the correct answer.
References:
* Delta UniForm for Apache Iceberg - Databricks Documentation
* Convert Delta to Iceberg - Databricks
NEW QUESTION # 173
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:
A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?
Answer: A
Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation , under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint
NEW QUESTION # 174
A table is registered with the following code:
Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?
Answer: D
NEW QUESTION # 175
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