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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Prepare and process data | 30-35% | - Data transformation and modeling
|
| Deploy and manage data pipelines and workloads | 30-35% | - Operational reliability
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
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76. Frage
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Antwort: A
Begründung:
Schema evolution allows the target Delta table to incorporate compatible new source columns instead of failing when the incoming schema changes. This is the appropriate response to additive schema drift and avoids manually rebuilding tables whenever the source evolves. Creating a separate table for every schema version would fragment the dataset and increase operational effort. Disabling schema enforcement removes valuable protection against incompatible or corrupt data rather than handling legitimate evolution safely. Row filters operate on records and cannot remove an unexpected column from the incoming schema. With schema evolution enabled, the pipeline can detect new fields, update the target schema, and continue processing while retaining Delta Lake's transactional guarantees. The solution therefore supports changing source data without sacrificing the managed-table architecture.
77. Frage
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
* asset.id
* asset, type
* asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The correct SQL statement uses the full three-part namespace finance.procurement.assets with the three specified columns.
In Unity Catalog, every object lives in a three-tier hierarchy: catalog # schema # table. Using the full path finance.procurement.assets guarantees the table lands in the right schema regardless of the session's current catalog or schema context. Omitting the catalog or schema name relies on the session default, which may not be finance.procurement - a silent mistake that's hard to catch.
The column names asset_id, asset_type, and asset_name must match the spec exactly. Unity Catalog applies access controls, lineage tracking, and tagging at the column level, so the names are meaningful beyond just the schema. Once created, any GRANT statements can target specific columns for fine-grained access control.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create- table-using
78. Frage
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1 You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence' To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
Explanation:
Registering an ADLS Gen2 account as an external location in Unity Catalog requires three steps in a specific order:
Step 1: Create a Databricks Access Connector. This Azure resource wraps a system-assigned or user-assigned managed identity. It is the credential-less authentication bridge between Databricks and Azure Storage - no SAS tokens or access keys are stored anywhere in the workspace.
Step 2: Create a Storage Credential in Unity Catalog that references the Access Connector. This object tells Unity Catalog 'use this identity when accessing storage.' The Storage Credential is the reusable authentication object.
Step 3: Create an External Location that maps a Unity Catalog path to the specific ADLS Gen2 container using the Storage Credential. This is the object that grants Databricks users access to files under that path, and it respects the principle of least privilege by scoping access to a specific container.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/storage-credentials
79. Frage
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.
Antwort:
Begründung:
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.
80. Frage
You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
Antwort: A
Begründung:
The Databricks CLI contains the bundle command group for validating, deploying, and running Declarative Automation Bundles. CLI commands can be executed consistently from developer terminals or CI/CD pipelines, making deployments automated, repeatable, and suitable for multiple target environments. The Databricks SDK for Python can manage workspace APIs programmatically, but it is not the standard bundle deployment interface required here. Git folders provide source-control integration inside the workspace but do not deploy bundle-defined resources. The Jobs UI supports interactive creation and management of jobs, which introduces manual steps and does not provide the same infrastructure-as-code workflow. Using commands such as databricks bundle validate and databricks bundle deploy -t dev directly satisfies the automated development deployment requirement. Microsoft Learn
81. Frage
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