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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Prepare and process data | 30–35% | - Ingest and transform data
|
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NEW QUESTION # 59
What improves join performance for small lookup tables?
Answer: B
NEW QUESTION # 60
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?
Answer: A
Explanation:
The correct answer is A. Photon is Azure Databricks' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso's requirement for 'fast and consistent performance for BI workloads' and 'production ingestion workloads that can scale automatically during telemetry spikes.' Photon integrates transparently - no code changes are needed - and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.
Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what's needed - telemetry spikes require elastic scaling, not a locked node count.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/photon
NEW QUESTION # 61
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake's MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn't delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
NEW QUESTION # 62
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs on a schedule and processes business-critical data.
You need to ensure that you receive a notification if Job1 fails.
What should you do?
Answer: D
Explanation:
Lakeflow Jobs provides built-in job notifications for important run events, including start, successful completion, failure, duration warnings, and streaming-backlog thresholds. Configuring a failure notification for Job1 ensures that the designated email address or system destination receives a notification when a scheduled run ends unsuccessfully. This is the most direct solution and requires no separate monitoring query or alert-processing infrastructure. Notifications can be delivered through email or configured destinations such as Microsoft Teams, Slack, PagerDuty, or an HTTP webhook. Microsoft Learn A SQL alert task evaluates the results of a SQL query and is not intended to monitor the execution state of the overall job. Sending results to Log Analytics would centralize diagnostic information but would not independently notify anyone unless an additional alerting solution was configured. An Azure Monitor alert could potentially be developed, but it would add unnecessary configuration and administration when Lakeflow Jobs already supports failure notifications. Therefore, configuring a job-run failure notification is the simplest and most appropriate solution.
NEW QUESTION # 63
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: B
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 # 64
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