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| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| Prepare and process data | 30–35% | - Ingest and transform data
|
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NEW QUESTION # 53
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: D
Explanation:
To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies.
Core Remedies
*-> Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user's activity.
Reference:
https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how- clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e
NEW QUESTION # 54
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?
Answer: A
Explanation:
The correct answer is C - a job cluster that uses autoscaling.
Job clusters (also called pipeline clusters in the SDP context) are created exclusively for a pipeline run and terminated when the pipeline stops. You pay only for what the pipeline uses, and there's no idle cost between runs. Autoscaling on a job cluster lets the pipeline expand during heavy processing and contract during lighter stages - the combination of on-demand lifecycle and elastic scaling gives the lowest operational cost.
Option A (all-purpose cluster) runs at a higher DBU rate and persists beyond the pipeline's lifecycle, meaning you're paying for it even when the pipeline isn't running. Option B (SQL warehouse) is optimised for interactive BI and ad-hoc queries, not for the batch/streaming workloads SDP pipelines run. Option D (single- node all-purpose) has no scaling, runs at the all-purpose DBU rate, and is capped at one node - unsuitable for any production pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/configure-pipeline
NEW QUESTION # 55
You need to ingest real-time IoT data into Delta Lake with exactly-once guarantees. Which approach should you use?
Answer: C
Explanation:
Structured Streaming with checkpointing ensures fault tolerance and exactly-once processing semantics in Databricks. It tracks processed offsets and recovers from failures automatically.
Batch ingestion cannot guarantee real-time processing. Copy activity is not designed for streaming workloads and manual ingestion is not scalable or reliable.
NEW QUESTION # 56
Which layer contains cleaned and conformed data in Databricks Lakehouse architecture?
Answer: B
Explanation:
Silver layer contains cleaned, validated, and enriched data ready for analytics. Bronze stores raw ingested data. Gold contains aggregated business-level data. Raw is not part of formal medallion architecture naming.
NEW QUESTION # 57
Your company has Sales, Finance, and HR departments.
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement Unity Catalog to meet the following requirements:
* Access to Unity Catalog for each department must be managed independently from that of the other departments.
* The data assets of each department must be logically isolated from those of the other departments.
* The solution must support centralized governance.
What should you do for each department?
Answer: C
Explanation:
Create a separate catalog for each department, such as Sales, Finance, and HR. A catalog is the primary unit of data organization and logical isolation in Unity Catalog. Each catalog can contain its own schemas, tables, views, volumes, functions, and models. Catalog-level ownership and permissions can be assigned independently, allowing each department's access to be administered without granting access to the other departments' assets. Microsoft Learn All three catalogs remain registered in the same Unity Catalog metastore, preserving centralized governance, auditing, lineage, discovery, and access-control administration. A separate storage account is unnecessary because the requirement specifies logical rather than physical isolation. Dynamic views provide row- or column-level filtering but do not create an independent organizational and permission boundary for all departmental assets. Creating separate workspaces would add administrative complexity and is unnecessary because catalogs provide the required isolation within a centrally governed metastore.
Therefore, creating one catalog for each department provides independent access management, logical data separation, and centralized governance with the least administrative overhead.
NEW QUESTION # 58
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