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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 2: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 4: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
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NEW QUESTION # 53
You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?
Answer: C
Explanation:
Lakeflow Jobs provides native orchestration for multi-task Databricks workflows. Separate ingestion, cleansing, and curation tasks can be connected through explicit dependencies, ensuring that each stage starts only after its required upstream work succeeds. Each task can also have independent retry, notification, timeout, and compute settings, directly addressing the pipeline's resiliency requirements. Azure Data Factory could orchestrate notebooks, but it introduces another service when Lakeflow Jobs already provides the required functionality. A single notebook makes failures harder to isolate and can force successful stages to be rerun. Independently scheduled jobs rely on timing assumptions rather than actual task completion and can fail when an upstream stage runs longer than expected. Explicit Lakeflow Jobs dependencies provide reliable execution order and centralized monitoring. Microsoft Learn
NEW QUESTION # 54
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 # 55
You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job1 that processes data continuously and fails periodically due to transient errors.
You need to ensure that Job1 meets the following requirements:
- Resumes processing from the point that Job1 failed
- Minimizes how long it takes to restart Job1
- Minimizes the costs to restart Job1
What should you do?
Answer: A
Explanation:
You must use checkpointing.
Checkpointing is the native Apache Spark mechanism designed specifically to handle failures in Structured Streaming jobs. It saves the exact execution state and progress to cloud storage (like Azure Data Lake Storage), allowing the job to resume precisely where it left off without data loss.
Resumes from Failure Point: The checkpoint directory stores the stream offsets. When restarted, Spark reads these offsets to pick up exactly where it failed.
Minimizes Restart Time: By saving the state, Spark does not need to recompute historical streaming data or re-evaluate the entire stream architecture from scratch.
Minimizes Restart Costs: It prevents the reprocessing of duplicate data, saving valuable cluster compute time and reducing cloud infrastructure costs.
Reference:
https://www.linkedin.com/posts/shilpa-das-ln_what-is-checkpointing-in-spark-checkpointing- activity-7297113790393815041-AhPg
NEW QUESTION # 56
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: C
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 # 57
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?
Answer: E
Explanation:
Delta Sharing is designed to share governed data securely with recipients outside an Azure Databricks workspace. The external organization can query the shared data from its own compatible compute platform without receiving workspace access or requiring a Databricks SQL warehouse. Unity Catalog centrally controls which tables, views, or other objects are included in the share and which recipients can access them.
Moving files to an SFTP server creates additional copies and requires custom transfer and security administration. Lakeflow Connect is intended for ingesting data into Databricks rather than sharing curated data externally. Granting workspace access or creating a SQL warehouse would require the recipient to use Databricks-managed resources. Delta Sharing therefore provides the required open access model, centralized governance, and minimal administrative effort.
NEW QUESTION # 58
......
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