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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 2: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Topic 4: Prepare and process data | 30-35% | - Data ingestion
|
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NEW QUESTION # 55
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: D
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 # 56
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 # 57
You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
What should you do?
Answer: A
Explanation:
A foreign catalog mirrors the external ERP database and provides read-only access through Lakehouse Federation. Consequently, it cannot host managed volumes or locally created Delta tables, and its foreign tables cannot be altered to add analytics columns. The appropriate design is to create governed views in the managed analytics catalog and have those views reference the foreign tables through fully qualified catalog.
schema.table names. This preserves the ERP data in its source system, avoids duplication, and exposes standardized curated objects through Unity Catalog. Permissions can then be granted on the managed views while access to underlying foreign objects remains controlled. Unity Catalog uses a three-level namespace, and foreign catalogs are specifically intended to make external database data queryable without copying it into Databricks-managed storage. Microsoft Learn
NEW QUESTION # 58
A Delta table receives new fields in incoming JSON data. You want to automatically adapt without breaking pipelines. What should you enable?
Answer: D
Explanation:
Auto Loader supports schema inference and schema evolution, allowing new columns to be added automatically during ingestion. This reduces pipeline maintenance and avoids failures due to schema drift. Disabling enforcement is unsafe and risks data corruption. Overwrite mode deletes existing data. Manual updates are not scalable.
NEW QUESTION # 59
Which feature helps reduce data scan during query execution in Delta Lake?
Answer: D
Explanation:
Delta Lake uses data skipping based on file-level statistics (min/max values). This reduces unnecessary file scans and improves query performance. VACUUM removes old files but does not improve query speed. Cluster restart has no impact on query optimization.
NEW QUESTION # 60
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