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| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
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NEW QUESTION # 52
You have an Azure Databricks workspace named Workspace1. You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster 1. The solution must use Unity Catalog for access control.
What should you do?
Answer: A
Explanation:
The correct answer is B. The %pip install command (or pip3 in a terminal context) creates an isolated, per- session library environment in notebooks, which is the Unity Catalog-compatible approach. Unity Catalog workspaces require cluster access mode set to 'Shared' or 'Single User,' and %pip installs work seamlessly within those modes without requiring cluster restarts.
Option A (custom dependency script) introduces extra maintenance work for every environment change - exactly what the question says to avoid. Option C installs libraries at the cluster level and requires a manual restart, which disrupts other users sharing the cluster and bypasses the per-notebook isolation model that Unity Catalog recommends. Option D uploads libraries to the Workspace file system (legacy DBFS approach), which is being deprecated in favour of Unity Catalog Volumes for library storage.
Reference: https://learn.microsoft.com/en-us/azure/databricks/libraries/notebooks-python-libraries
NEW QUESTION # 53
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: D
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 # 54
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: A
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 # 55
Which component enforces table-level permissions in Databricks?
Answer: B
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 # 56
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed.
You need to configure the job alerting behavior.
What should trigger a notification?
Answer: A
Explanation:
To achieve this, you should set a "Failure" notification at the individual task level for critical tasks only, rather than relying on a job-level notification.
Task-Level Notifications (Critical Tasks): By configuring notifications exclusively on the critical tasks for the "Failure" event status, any failure of these specific tasks will immediately send an alert. Because tasks execute within the job lifecycle, this notification inherently triggers while the job is in progress or completing its critical paths.
No Job-Level Notifications: You must not set a job-level "Failure" notification. If a non-critical task fails, it can cause the overall job status to register as a failure or "Succeeded with failures", which would erroneously trigger a job-level alert.
Handling Non-Critical Tasks: Non-critical tasks should be left without task-level failure notifications. To ensure they don't break downstream flows, you can configure downstream dependent tasks to use the "Run if: All done" or "Run if: None failed" (depending on your dependency architecture) conditional rules so the job execution continues and logs the failures automatically to the matrix view.
Reference:
https://learn.microsoft.com/en-us/azure/databricks/jobs/notifications
NEW QUESTION # 57
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