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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Topic 4: Prepare and process data | 30-35% | - Data quality and validation
|
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NEW QUESTION # 29
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: C
Explanation:
To query tables governed by Unity Catalog, SP1 requires permissions at every relevant level of the three-level namespace. USE CATALOG permits SP1 to interact with objects inside Catalog1 and Catalog2. USE SCHEMA permits it to access the schemas contained in those catalogs. Finally, SELECT authorizes SP1 to read data from the tables and views.
Because Unity Catalog supports privilege inheritance, granting USE SCHEMA and SELECT at the catalog level applies those permissions to the applicable current and future child objects. This is appropriate when SP1 must query data throughout both catalogs. These permissions provide read-only access and do not allow SP1 to create objects or modify and delete data. Microsoft Learn Option B is missing USE CATALOG. Option C provides namespace access but does not authorize data retrieval. Option D lacks USE SCHEMA, which is required to interact with objects inside each schema.
Therefore, SP1 requires USE CATALOG, USE SCHEMA, and SELECT.
NEW QUESTION # 30
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?
Answer: D
Explanation:
Job1 should contain one ingestion task that acts as the common upstream dependency for two separate transformation tasks. Once ingestion succeeds, Lakeflow Jobs can start both downstream tasks concurrently because neither transformation depends on the other. This design represents the actual workflow, avoids duplicated ingestion, and reduces total execution time through parallelism. Creating two ingestion tasks would repeat the same source processing and could introduce inconsistent results or unnecessary costs. A single sequential task would prevent parallel transformation and make failures harder to isolate and retry. Defining three independent tasks without dependencies could allow transformations to start before ingestion has completed. An explicit directed task graph therefore provides the required execution order while preserving parallelism for independent downstream processing.
NEW QUESTION # 31
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?
Answer: C
Explanation:
A Databricks notebook provides the flexibility required to implement complex, highly customized Python and PySpark transformations. Scheduling that notebook as a Lakeflow Jobs task supplies native daily orchestration, monitoring, retries, and compute management without introducing another service. Azure Data Factory data flows are oriented toward visually designed transformations and would add external orchestration complexity for logic already implemented most naturally in Python. A continuous job is inappropriate because the workload runs once per day rather than continuously. Spark Declarative Pipelines is effective for declarative batch and streaming ETL, but it is less direct when the core requirement emphasizes highly customized procedural Python transformations. A notebook task therefore provides the necessary programming freedom while keeping scheduling and operation inside Azure Databricks.
NEW QUESTION # 32
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
* Ensure that amount is always greater than 0.
* Fail the pipeline update for Table1 when data that violates rule1 is detected.
Which statement should you execute?
Answer: A
Explanation:
The expect_or_fail decorator validates every incoming record against the expression amount > 0 and stops the table update when a violation occurs. This exactly meets the requirement that invalid data cause the pipeline update to fail. Option A applies multiple expectations and drops violating records instead of failing the update. Option B also discards invalid records while allowing valid records to continue processing. Option D records expectation metrics but retains invalid records and allows the update to continue. These alternative behaviors are useful when invalid data can be tolerated, quarantined, or monitored, but they do not satisfy strict enforcement. The fail action provides transactional protection because the affected update does not successfully publish data that violates the stated quality rule.
NEW QUESTION # 33
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: D
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 # 34
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