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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| Prepare and process data | 30–35% | - Optimize and manage data storage
|
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NEW QUESTION # 28
You have an Azure Databricks workspace that contains multiple all-purpose clusters. You discover that some clusters remain idle for long periods after users finish their work. You need to reduce compute costs without affecting active workloads. What should you do?
Answer: B
Explanation:
The correct answer is D - configure automatic termination.
The problem is specific: clusters sit idle after users finish working but nobody manually shuts them down.
Automatic termination solves this directly - once a cluster has been idle for the configured period (no running commands, no attached notebooks with active execution), it shuts itself down. You eliminate the idle cost without any manual intervention and without affecting any workload that is actually running.
Option A (convert to job clusters) would force users off interactive all-purpose clusters, disrupting their development workflow. Option B (spot instances) reduces the hourly rate while a cluster is running but does nothing about the idle-time problem - a cheaper idle cluster is still waste. Option C (enable autoscaling) reduces the number of workers during light load but keeps the cluster alive at the minimum node count. It saves some cost but doesn't fully eliminate idle spend the way auto-termination does.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#auto-termination
NEW QUESTION # 29
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
Answer: A
Explanation:
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/
NEW QUESTION # 30
You have an Azure Databticks workspace that contains an all-purpose compute cluster named Cluster1.
Cluser1 is used for
interactive development.
You need to configure Cluster1 to meet the following requirements:
* Automatically add and remove worker nodes based on workload demand
* Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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:
Explanation:
Two separate cluster settings address the two requirements:
Autoscaling handles the first requirement - dynamically adding workers when the workload is heavy and removing them when it lightens. You set a minimum and maximum node count, and Databricks adjusts the cluster size between those bounds based on task queue depth.
Auto-termination handles the second - the cluster shuts itself down after a configurable idle period (e.g., 30 minutes with no active queries), preventing wasted spend on a development cluster left running overnight.
These two settings are independent and complementary: autoscaling manages horizontal elasticity during active use, while auto-termination manages complete shutdown during inactivity. Both are configured in the cluster creation UI under the Compute section.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#autoscaling
NEW QUESTION # 31
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:
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 # 32
Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 33
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