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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 2: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 4: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
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NEW QUESTION # 51
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?
Answer: E
Explanation:
Delta Sharing is designed to share governed data securely with recipients outside an Azure Databricks workspace. The external organization can query the shared data from its own compatible compute platform without receiving workspace access or requiring a Databricks SQL warehouse. Unity Catalog centrally controls which tables, views, or other objects are included in the share and which recipients can access them.
Moving files to an SFTP server creates additional copies and requires custom transfer and security administration. Lakeflow Connect is intended for ingesting data into Databricks rather than sharing curated data externally. Granting workspace access or creating a SQL warehouse would require the recipient to use Databricks-managed resources. Delta Sharing therefore provides the required open access model, centralized governance, and minimal administrative effort.
NEW QUESTION # 52
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 # 53
You have an Azure Databricks solution that was developed by multiple engineers and deployed to development, test, and production environments.
You need to implement an application lifecycle management (ALM) process that meets the following requirements:
* Deploys and provides source control for Databricks notebooks, workflows, and jobs in a consistent and repeatable way.
* Parameterizes the target deployment environment.
What should you include in the solution?
Answer: C
Explanation:
Declarative Automation Bundles provide an infrastructure-as-code approach for defining, deploying, and managing complete Azure Databricks projects. A bundle can contain notebooks and other source files together with resource definitions for Lakeflow Jobs, pipelines, dashboards, models, and related workspace resources. Storing the bundle in Git provides source control, collaboration, code review, and integration with automated CI/CD workflows.
Bundle targets can define environment-specific values and overrides for development, test, and production.
The same source-controlled project can therefore be deployed repeatedly to different environments without manually modifying its core definitions. This directly satisfies the requirement to parameterize the target deployment environment. Microsoft Learn Git folders provide source control but manual promotion is inconsistent and error-prone. Terraform is primarily intended for provisioning infrastructure and workspace resources rather than packaging the entire Databricks application lifecycle, including its source files. Exporting and importing workspaces is also a manual process that does not provide a reliable parameterized deployment model. Therefore, Declarative Automation Bundles stored in Git are the appropriate solution.
NEW QUESTION # 54
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 # 55
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 # 56
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