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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 quality and validation
|
| Deploy and manage data pipelines and workloads | 30-35% | - Operational reliability
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
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NEW QUESTION # 76
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?
Answer: C
Explanation:
Repairing the current job run allows Lakeflow Jobs to rerun the failed task while preserving the successful status of tasks that already completed. This prevents several hours of successful processing from being repeated and reduces both recovery time and compute consumption. If downstream tasks had been skipped because of the failure, they can also be included in the repair operation as appropriate. Updating job parameters changes configuration but does not retry the failed task in the existing run. Restarting Job1 creates a new run and ordinarily executes the workflow again from its beginning. Disabling and reenabling the schedule only changes whether future scheduled runs can start; it does not repair the failed execution. Repair Run is specifically designed for recovering selected failed or skipped tasks in a multi-task workflow.
NEW QUESTION # 77
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
Answer: C
Explanation:
The symptoms indicate data skew. Spark partitions the stage's work into tasks, but a few partitions contain substantially more records than the others. Tasks processing the smaller partitions finish quickly, leaving the cluster underutilized while the stage waits for the oversized partitions. Caching would concern whether reused data is stored in memory and does not inherently explain an uneven distribution of input records. Shuffling transfers data between executors and can be expensive, but the decisive evidence is that only a few tasks process far more records. Spilling occurs when an executor cannot keep intermediate data in memory and writes it to disk; the scenario does not identify high spill volumes. Examining per-task input sizes in the Spark UI would confirm the skewed partition distribution.
NEW QUESTION # 78
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 # 79
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?
Answer: C
Explanation:
The correct answer is A - merge.
A Git merge combines the histories of two branches by creating a merge commit that joins them. Every individual commit from Branch1 remains visible in the repository log - the full development history is preserved. This is the requirement: 'the solution must preserve the commit history in the repository.' Option C (rebase) moves Branch1's commits on top of main by replaying them as new commits with new hashes. The end result looks like a linear history, but the original commit hashes are rewritten - the prior history is not preserved in its original form. For a shared repository, rebase rewrites public history, which is considered problematic.
Option B (pull) fetches remote changes and merges or rebases them into the current branch - it's used to sync with a remote, not to incorporate a feature branch. Option D (push) sends local commits to the remote but doesn't incorporate any branch into another.
Reference: https://learn.microsoft.com/en-us/azure/databricks/repos/git-operations-with-repos
NEW QUESTION # 80
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two things are needed here:
For the rule enforcement: use @dlt.expect_or_drop. This drops any record that violates rule1 before it reaches Table1, while the pipeline continues processing all valid records. The table only ever receives clean data.
For reviewing metrics: the Lakeflow SDP Pipeline UI is the right tool - zero development effort required.
The pipeline graph shows expectation pass/fail counts directly on each table node, and the event log provides a detailed per-batch breakdown of how many records were dropped and why. Data engineers can inspect this at any time without writing additional monitoring queries or connecting external dashboards.
This combination is one of the strongest arguments for SDP over hand-coded Structured Streaming:
expectation observability is built in, not bolted on.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations
NEW QUESTION # 81
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