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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
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NEW QUESTION # 26
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
Answer: A
Explanation:
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/
NEW QUESTION # 27
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes curated Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
Answer: A
Explanation:
The best choice is a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
Low Development Effort: Lakeflow SDP (formerly known as Delta Live Tables or DLT) is a completely declarative ETL framework. You simply define the target schemas and data transformations using standard SQL or Python. Databricks automatically manages the underlying operational complexities, state maintenance, task orchestration, and DAG dependencies for you.
Built-in Quality & Monitoring: It offers out-of-the-box data monitoring capabilities via Expectations, which allow you to specify data cleansing policies (like drop, retain, or fail on bad rows) with zero custom validation code. It also captures complete, automatic end-to-end data lineage and operational stats straight into Unity Catalog.
Built-in Resilience: Infrastructure failure handling and automatic retries are natively managed by the Lakeflow runtime.
Native Storage Ingestion: Using read_files() (Auto Loader) within SDP allows effortless, incremental ingestion of files from cloud object storage directly into curated Delta tables.
Reference:
https://docs.databricks.com/aws/en/ldp/
NEW QUESTION # 28
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?
Answer: A
Explanation:
Repairing the current job run allows Lakeflow Jobs to rerun the failed task while preserving the successful status of tasks that already completed. This prevents several hours of successful processing from being repeated and reduces both recovery time and compute consumption. If downstream tasks had been skipped because of the failure, they can also be included in the repair operation as appropriate. Updating job parameters changes configuration but does not retry the failed task in the existing run. Restarting Job1 creates a new run and ordinarily executes the workflow again from its beginning. Disabling and reenabling the schedule only changes whether future scheduled runs can start; it does not repair the failed execution. Repair Run is specifically designed for recovering selected failed or skipped tasks in a multi-task workflow.
NEW QUESTION # 29
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?
Answer: A
Explanation:
The query will output a single column containing only the values that are duplicated within Table1 and also duplicated within Table2.
Duplicates Isolated First: The GROUP BY and HAVING COUNT(*) > 1 clauses filter each table independently to find only their duplicate rows.
Matching Values Found: The INTERSECT operator compares the filtered results of both tables.
Shared Values Returned: Only values that exist in both filtered datasets are included in the final output.
Automatic Distinct Filtering: INTERSECT automatically removes any duplicate values from the final result set, ensuring each matching value appears exactly once.
Column Naming: The output column will inherit its name from the first SELECT statement (Column1).
Reference:
https://quizlet.com/study-guides/inf3707-chapter-4-data-retrieval-from-2-or-more-tables-
6ec17b26-76a8-4c33-8fa7-18d64154512d
NEW QUESTION # 30
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 Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?
Answer: B
Explanation:
The correct answer is B - define a pipeline expectation.
SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline's event log as expectation violations - available for review without any extra code.
Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code.
Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views - they're not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the 'pipeline continues to run' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations
NEW QUESTION # 31
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