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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15โ20% | - Manage data sharing and permissions
|
| Topic 2: Deploy and maintain data pipelines and workloads | 30โ35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 3: Prepare and process data | 30โ35% | - Ingest and transform data
|
| Topic 4: Set up and configure an Azure Databricks environment | 15โ20% | - Integrate with Azure services
|
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NEW QUESTION # 92
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: D
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 # 93
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
metastore1 contains:
* A catalog named Sales
* A schema named Customers in the Sales catalog
* A table named Customer_details in the Customers schema
You need to ensure that a user named User1 can update the data in Customer_details. The solution must meet the following requirements:
* Ensure that User1 cannot create new tables.
* Follow the principle of least privilege.
Which permission should you grant to User1 for each object? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
User1 requires permissions at each level of the Unity Catalog hierarchy. USE CATALOG allows User1 to interact with objects contained in the Sales catalog. USE SCHEMA permits access to objects within the Customers schema. Neither permission independently grants access to the data.
At the table level, MODIFY permits User1 to insert, update, and delete data in Customer_details. It does not grant permission to create new tables. The CREATE TABLE privilege is deliberately not granted on the Customers schema, ensuring that User1 cannot create tables. MANAGE would provide unnecessarily broad administrative capabilities, including the ability to manage permissions, and would violate the principle of least privilege. SELECT provides read access but does not authorize updates. Azure Databricks Therefore, the minimum permissions available in the answer area are USE CATALOG on Sales, USE SCHEMA on Customers, and MODIFY on Customer_details.
NEW QUESTION # 94
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. Table! is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table!
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Answer: C
Explanation:
The correct answer is C - Enable schema evolution.
When new columns are added to the source data, a pipeline without schema evolution treats the unexpected columns as a schema mismatch and fails the write. Schema evolution, when enabled in an SDP pipeline, automatically adds those new columns to the target Delta table on the next pipeline run. The pipeline continues without intervention, and no historical data is lost.
Option A (disable schema enforcement) is the wrong lever - it removes all schema validation, which could allow corrupt or mistyped data into Table1. Schema evolution is a targeted, safer response.
Option B (row filters to exclude records with new columns) would silently discard valid records just because they carry extra fields - that's data loss. Option D (separate table per schema version) creates an explosion of tables as schemas evolve and makes downstream analytics significantly more complex. Schema evolution is the clean, built-in solution.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/schema-evolution
NEW QUESTION # 95
Drag and Drop Question
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.
Answer:
Explanation:
NEW QUESTION # 96
You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
The correct answers are B and E.
The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:
Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel - in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.
Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting - the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.
Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
NEW QUESTION # 97
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