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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15โ20% | - Implement data governance and security
|
| Deploy and maintain data pipelines and workloads | 30โ35% | - Build and orchestrate pipelines
|
| Set up and configure an Azure Databricks environment | 15โ20% | - Select and configure compute resources
|
| Prepare and process data | 30โ35% | - Ingest and transform data
|
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NEW QUESTION # 14
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: C
Explanation:
The correct answer is B - a job failure.
The requirement draws a clear line: non-critical task failures should be logged silently; notifications should only fire when a critical failure causes the whole job to stop. Configuring the alert on ' Job Failure ' achieves this precisely - the notification triggers when the job itself reaches a Failed terminal state, which only happens when at least one critical task has failed and the job cannot complete.
Option A (task failure) would send a notification for every task-level failure, including non-critical ones. That
' s exactly the noise the question wants to avoid. Option C (job success) would never alert on failures at all.
Option D (task success) confirms completion but doesn ' t catch failures.
Setting alerting at the job level rather than the task level is also simpler to configure - you don ' t need to mark individual tasks as critical or non-critical in the notification settings.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/alerts
NEW QUESTION # 15
Which operation guarantees ACID compliance in Delta Lake?
Answer: B
Explanation:
Delta Lake ensures ACID compliance through its transaction log (Delta log). It tracks all changes, enabling consistency, isolation, and rollback capabilities. File append operations alone are not transactional. RDD transformations are low-level and not ACID-aware.
NEW QUESTION # 16
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?
Answer: B
Explanation:
The correct answer is B - No.
This is a common Python-to-PySpark trap. In pure Python, comparing a value to None with != works as expected. In PySpark, null comparisons follow SQL null semantics: any comparison involving NULL returns NULL (not True or False). So df.filter(df.order_amount != None) doesn't evaluate to True for non-null rows
- the comparison itself returns NULL for null values, and Spark interprets NULL in a filter as False, effectively dropping null rows. But the behaviour is undefined in edge cases and is not the documented approach.
More practically, Python's None and Spark's SQL NULL are different concepts. PySpark Column objects don't support Python's native equality/inequality semantics for null checking. The result is typically an empty DataFrame or incorrect filtering behaviour.
Always use .isNotNull() or .isNull() for null checks in PySpark column expressions. These methods are specifically designed for SQL-null-aware comparisons and produce correct, predictable results.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 17
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements;
* The cluster must scale up automatically when workloads increase.
* The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
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 C and D. Together they deliver cost-efficient autoscaling:
D (Enable autoscaling) allows the cluster to grow when workloads increase and shrink when they ease off.
This satisfies both scale-up and scale-down requirements without manual intervention.
C (Auto-termination after 30 minutes of inactivity) ensures the cluster stops entirely when no work is running, eliminating the cost of an idle cluster. This is the cheapest possible state.
Option A (disable Photon) reduces compute acceleration - that's a performance regression with no meaningful cost benefit for autoscaling. Option B (compute policy that lets users manage settings) adds governance overhead and doesn't address scaling behaviour. Option E (fixed number of workers) is the opposite of autoscaling - a static worker count that either over-provisions during quiet periods or under- provisions during peaks.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#autoscaling
NEW QUESTION # 18
You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! 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:
The correct answer is B - Repair the current job run.
Repair Run is designed for exactly this situation: a long-running job where most tasks succeeded but the final task failed due to a transient issue. Instead of restarting the entire eight-task job from the beginning - wasting hours of compute - Repair Run re-executes only the failed task and any dependents that were skipped as a result. All tasks that completed successfully are marked done and their outputs are reused.
Option A (update job parameters) changes configuration for future runs but doesn't re-execute the failed task in the current run. Option C (Restart Job1) re-runs every task from the start - precisely what the question says to avoid. Option D (disable and re-enable the schedule) creates a brand-new run on the next schedule trigger, again starting from the beginning rather than repairing the existing run.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures
NEW QUESTION # 19
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