DP-750 Interactive Questions - DP-750 Passed

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Set up and configure an Azure Databricks environment	15-20%	- Create and configure Azure Databricks workspaces 1. Manage Databricks runtimes 2. Configure compute resources and clusters 3. Configure networking and connectivity 4. Configure workspace settings
Deploy and maintain data pipelines and workloads	30-35%	- Manage production workloads 1. Implement CI/CD processes 2. Deploy workloads using Databricks Asset Bundles 3. Optimize workload performance and reliability 4. Monitor and troubleshoot pipelines 5. Maintain production data engineering solutions 6. Integrate Git-based development workflows 7. Create and manage Lakeflow Jobs
Secure and govern Unity Catalog objects	15-20%	- Implement governance and security 1. Implement access control and permissions 2. Manage data lineage and auditing 3. Configure Unity Catalog 4. Manage catalogs, schemas, and tables 5. Implement data-sharing capabilities
Prepare and process data	30-35%	- Ingest and transform data 1. Model and partition data 2. Implement streaming data processing 3. Transform data using SQL and Python 4. Apply medallion architecture patterns 5. Use Auto Loader and batch ingestion 6. Optimize storage and table performance 7. Implement data quality controls 8. Implement Delta Lake tables

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q97-Q102):

NEW QUESTION # 97
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to 'Skip' (or 'Allow only one concurrent run'). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to 'Skip' is 'Wait' (queue the new run), which ensures every scheduled run eventually executes
- but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs

NEW QUESTION # 98
You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! takes multiple hours to complete During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?

A. Update the job parameters.
B. Repair the current job run.
C. Restart Job!
D. Disable and reenable the job schedule.

Answer: B

Explanation:
The correct answer is B - Repair the current job run.
Repair Run is designed for exactly this situation: a long-running job where most tasks succeeded but the final task failed due to a transient issue. Instead of restarting the entire eight-task job from the beginning - wasting hours of compute - Repair Run re-executes only the failed task and any dependents that were skipped as a result. All tasks that completed successfully are marked done and their outputs are reused.
Option A (update job parameters) changes configuration for future runs but doesn't re-execute the failed task in the current run. Option C (Restart Job1) re-runs every task from the start - precisely what the question says to avoid. Option D (disable and re-enable the schedule) creates a brand-new run on the next schedule trigger, again starting from the beginning rather than repairing the existing run.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures

NEW QUESTION # 99
Which feature provides end-to-end tracking of data movement in Databricks?

A. Spark logs
B. Cluster metrics
C. Unity Catalog lineage
D. Query history only

Answer: C

Explanation:
Unity Catalog provides data lineage tracking across ingestion, transformation, and consumption layers. It helps visualize dependencies and compliance auditing. Spark logs are low-level and not structured for lineage. Query history is partial only.

NEW QUESTION # 100
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?

A. Create separate tasks for ingestion, cleansing, and curation.
B. Create three tasks that each contains the identical logic and use task retries.
C. Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script.
D. Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands.

Answer: A

Explanation:
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/

NEW QUESTION # 101
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group1.
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:
- Can create tables in schema1
- Can modify and query tables
- Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 102
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