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| Section | Weight | Objectives |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
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14. Frage
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?
Antwort: C
Begründung:
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
15. Frage
Which Azure service is best integrated with Databricks Unity Catalog for centralized data governance?
Antwort: B
Begründung:
Microsoft Purview integrates with Unity Catalog to provide centralized data governance, classification, and lineage tracking. It helps organizations manage data compliance and discovery. Key Vault handles secrets, not governance. DevTest Labs is for testing environments.
Azure Automation is for workflow automation.
16. Frage
You have an Azure Databricks workspace.
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically.
You need to configure compute for the pipeline. The solution must minimize operational costs and effort.
What should you use?
Antwort: A
Begründung:
The best option for a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically while keeping costs and administrative effort low is a job cluster that uses autoscaling.
Lowest Costs: Job clusters (also called automated compute) are billed at a significantly lower Data Processing Unit (DBU) rate compared to all-purpose clusters. By enabling autoscaling, Databricks dynamically allocates or removes worker nodes based on real-time pipeline demand, ensuring you never pay for unutilized resources.
Low Administrative Effort: While Databricks generally recommends Serverless compute as the absolute ideal for zero-admin pipelines, when selecting from classic compute options, a job cluster automatically handles its own lifecycle. It deploys when the pipeline starts executing and terminates automatically when processing is finished.
Incorrect:
[Not A]
Databricks SQL warehouses are designed to run standalone materialized views and streaming tables via standard SQL. They are not the native compute vehicle for running a fully automated, dedicated Lakeflow Spark Declarative Pipelines (SDP) deployment framework.
[Not B]
All-purpose compute is meant for interactive development, debugging, and ad-hoc analysis. It is billed at a much higher DBU rate, which violates the requirement to keep costs low.
[Not D]
Aside from the higher billing rate of all-purpose compute, a single-node configuration does not scale horizontally. This directly conflicts with your requirement to build a pipeline that scales automatically.
Reference:
https://docs.databricks.com/gcp/en/ldp/auto-scaling
17. Frage
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?
Antwort: A
Begründung:
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
18. Frage
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Antwort: A
Begründung:
Schema evolution allows the target Delta table to incorporate compatible new source columns instead of failing when the incoming schema changes. This is the appropriate response to additive schema drift and avoids manually rebuilding tables whenever the source evolves. Creating a separate table for every schema version would fragment the dataset and increase operational effort. Disabling schema enforcement removes valuable protection against incompatible or corrupt data rather than handling legitimate evolution safely. Row filters operate on records and cannot remove an unexpected column from the incoming schema. With schema evolution enabled, the pipeline can detect new fields, update the target schema, and continue processing while retaining Delta Lake's transactional guarantees. The solution therefore supports changing source data without sacrificing the managed-table architecture.
19. Frage
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