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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
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NEW QUESTION # 11
Which component enforces table-level permissions in Databricks?
Answer: D
Explanation:
Unity Catalog provides fine-grained access control at table, schema, and column levels. It centralizes governance across workspaces. Cluster policies control compute settings. Spark configuration does not manage security. DBFS permissions are not sufficient for enterprise governance.
NEW QUESTION # 12
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 Delta table named Table1 Table1 stores order data and contains two columns named Ordertd and Quantity that have the following requirements:
* Orderld must always be present.
* Quantity must be greater than 0.
You need to enforce the requirements during ingestion by using validation checks. The solution must evaluate each record independently and minimize custom code.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Lakeflow pipeline expectations provide declarative row-level data-quality validation. An expectation uses a SQL Boolean expression that is evaluated independently for every record passing through the pipeline. This minimizes custom code because the required validation can be expressed directly as a constraint rather than implemented through a separate validation process or custom function. Microsoft Learn The correct expression is OrderId IS NOT NULL AND Quantity > 0. The first condition enforces the requirement that every order record contains an OrderId. The second condition ensures that Quantity is strictly greater than zero. OrderId > 0 would impose an additional numeric-range requirement that the question does not specify. Checking only Quantity > 0 would fail to validate the required presence of OrderId. Checking Quantity IS NOT NULL AND OrderId > 0 reverses the stated requirements.
Aggregate checks operate across collections of records rather than validating each record independently.
Schema inference determines data structure and types but does not enforce these business-value requirements.
Therefore, a row-level validation check containing both required conditions is the correct solution.
NEW QUESTION # 13
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
NEW QUESTION # 14
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
Answer: C
Explanation:
The databricks bundle init command initializes a new bundle project from a selected default or custom project template. It prompts for relevant project values and creates the local project structure and starter configuration files. It does not deploy resources; deployment is performed with databricks bundle deploy. It does not merely validate an existing configuration, because validation uses databricks bundle validate. Running deployed bundle resources requires databricks bundle run followed by the resource key. The distinction matters in an automated bundle lifecycle: initialization scaffolds the project, validation checks configuration, deployment creates or updates workspace resources, and run starts a configured job or pipeline. Therefore, option A accurately describes the initialization command's function. Microsoft Learn
NEW QUESTION # 15
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group1.
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:
- Can create tables in schema1
- Can modify and query tables
- Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 16
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