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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30โ35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15โ20% | - Manage data sharing and permissions
|
| Deploy and maintain data pipelines and workloads | 30โ35% | - Monitor, troubleshoot, and maintain workloads
|
| Set up and configure an Azure Databricks environment | 15โ20% | - Integrate with Azure services
|
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NEW QUESTION # 48
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to profile a table to meet the following requirements:
* The count of null values per column must be evaluated repeatedly as new records are added to the table.
* Changes in the count of null values must be observable over the progression of the dataset.
Which type of profile should you create?
Answer: B
Explanation:
A time series profile repeatedly calculates data-quality metrics as a table changes and preserves those measurements over time. This makes it possible to observe whether the number of null values in each column is increasing, decreasing, or remaining stable as new records are added. A snapshot profile evaluates the table at a particular point in time and is therefore insufficient when the requirement is to analyze metric progression across multiple updates. Inference is associated with deriving information such as a schema or statistical characteristics; it is not the profile type used to maintain a historical sequence of monitoring measurements.
Because the question requires repeated evaluation and the ability to observe changes throughout the dataset's progression, the time series profile satisfies both requirements.
NEW QUESTION # 49
You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?
Answer: C
Explanation:
Installing a Maven package on every node requires changing Cluster1's library configuration. The CAN MANAGE permission grants the cluster-level management capabilities required to add or remove libraries without giving User1 broader control over the entire workspace or Azure resource group. CAN RESTART allows a user to restart the cluster but does not provide sufficient authority to change its library configuration.
Assigning the Azure Contributor role would grant excessive control over the resource group and does not represent the appropriate Databricks object-level permission. Making User1 a workspace administrator would provide substantially more privileges than necessary. Granting CAN MANAGE directly on Cluster1 therefore satisfies the operational requirement while limiting User1's authority to the specific compute resource that must be modified.
NEW QUESTION # 50
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 is written by batch jobs every hour and is queried frequently by filtering two columns named Customerid and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 51
What ensures failure recovery in Databricks Structured Streaming?
Answer: A
Explanation:
Checkpointing stores streaming state and progress, allowing recovery after failures without data loss or duplication. Auto scaling adjusts compute resources but does not ensure reliability.
Partition pruning improves query performance. Broadcast joins optimize joins but are unrelated to recovery.
NEW QUESTION # 52
You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?
Answer: B
Explanation:
The correct answer is D - Continuous trigger.
A Continuous trigger keeps the job running as a perpetual loop. As soon as one micro-batch or iteration completes, the next begins. New records are picked up with the shortest possible latency - as close to real- time as a Lakeflow Jobs pipeline gets.
File Arrival (Option B) is event-driven but introduces per-file trigger overhead and is best suited for file-based ingestion rather than continuous streaming workloads. Scheduled (Option C) runs at fixed clock intervals - if new data arrives between runs, it waits until the next scheduled execution. Manual (Option A) requires a human to start each run.
The question specifies serverless compute, which pairs naturally with Continuous trigger because serverless handles cluster lifecycle automatically - the job stays active without managing a persistent cluster. 'New records must be processed as soon as they become available' is the exact use case the Continuous trigger is designed for.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/triggers
NEW QUESTION # 53
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