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| Section | Weight | Objectives |
|---|---|---|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Prepare and process data | 30-35% | - Data quality and validation
|
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NEW QUESTION # 87
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
- Invalid records must NOT be written to Table1.
- Invalid records must be preserved for review.
- Minimize development effort.
What should you do?
Answer: B
Explanation:
To meet your pipeline requirements, use the expect_or_drop expectation constraint to ensure invalid records are not written to the table while allowing the pipeline to continue running. To preserve and review invalid records without custom development, capture the failed rows by isolating the inverse rule (expect_or_drop) in a quarantine flow.
Summary of the Behavior Matrix
Pipeline continues to run: Achieved via drop or warn behaviors (the fail policy is excluded because it halts execution).
Invalid records not written to the clean table: Achieved via the drop policy, which discards records from the downstream dataset.
Records preserved for review: Achieved by querying the automated, built-in Lakeflow Event Log Delta table, which automatically captures the details of any rejected or flagged rows without requiring custom code.
Minimized development: Eliminates the need to construct secondary "quarantine" storage tables manually.
Reference:
https://docs.databricks.com/aws/en/ldp/best-practices
NEW QUESTION # 88
You have an Azure Databricks workspace that uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
Salesperson
Item
2019
2020
2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);
Answer:
Explanation:
Explanation:
First dropdown: Year
Second dropdown: (2019, 2020, 2021)
The UNPIVOT operator converts the separate 2019, 2020, and 2021 columns into rows. Value becomes the output column containing the values previously stored in those year columns. Year becomes the output name column that identifies the original column from which each value came. Therefore, the expression must use Value FOR Year IN (2019, 2020, 2021). The Salesperson and Item columns are not included in the IN list because they remain identifier columns and are repeated for every resulting year row. A single source row consequently produces three output rows-one for each listed year. Selecting (Year) would reference an output name rather than the source columns that must be rotated.
NEW QUESTION # 89
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries
NEW QUESTION # 90
What improves join performance for small lookup tables?
Answer: B
Explanation:
Broadcast joins send the small table to all worker nodes, avoiding expensive shuffling. This significantly improves performance. Shuffle and sort merge joins are heavier. Cartesian joins are inefficient and generally avoided.
NEW QUESTION # 91
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?
Answer: D
Explanation:
To ensure a service principal can query data in two specific catalogs while maintaining the principle of least privilege, you must assign the following permissions for each catalog:
Unified Catalog Permissions
USE CATALOG on the specific catalog: This is a prerequisite that allows the principal to see the catalog and its child objects.
USE SCHEMA on the schemas containing the data: This allows the principal to interact with the schemas within that catalog.
SELECT on the specific tables or views: This provides the actual read access required to query the data.
Reference:
https://learn.microsoft.com/en-us/purview/data-governance-roles-permissions
NEW QUESTION # 92
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