Quiz DP-750 - Implementing Data Engineering Solutions Using Azure Databricks Marvelous Top Dumps

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Secure and govern Unity Catalog objects15–20%- Implement data governance and security
  • 1. Enforce data quality, lineage, and auditing
  • 2. Configure access control: row-level, column-level, attribute-based security
  • 3. Manage catalogs, schemas, tables, views, and volumes
- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials
Topic 2: Set up and configure an Azure Databricks environment15–20%- Integrate with Azure services
  • 1. Configure monitoring with Azure Monitor and diagnostic settings
  • 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
- Select and configure compute resources
  • 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 2. Configure cluster policies, instance pools, and libraries
  • 3. Manage workspace settings, permissions, and networking
Topic 3: Deploy and maintain data pipelines and workloads30–35%- Build and orchestrate pipelines
  • 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 3. Design and implement Lakeflow Spark Declarative Pipelines
- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Troubleshoot failures, repair and restart jobs
  • 3. Apply SDLC practices and version control
Topic 4: Prepare and process data30–35%- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Implement lakehouse architecture and manage table versions
  • 3. Handle structured, semi-structured, and unstructured data
- Ingest and transform data
  • 1. Ingest batch and streaming data from multiple sources
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which layer contains cleaned and conformed data in Databricks Lakehouse architecture?

Answer: D

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 # 17
You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?

Answer: A

Explanation:
The Databricks CLI contains the bundle command group for validating, deploying, and running Declarative Automation Bundles. CLI commands can be executed consistently from developer terminals or CI/CD pipelines, making deployments automated, repeatable, and suitable for multiple target environments. The Databricks SDK for Python can manage workspace APIs programmatically, but it is not the standard bundle deployment interface required here. Git folders provide source-control integration inside the workspace but do not deploy bundle-defined resources. The Jobs UI supports interactive creation and management of jobs, which introduces manual steps and does not provide the same infrastructure-as-code workflow. Using commands such as databricks bundle validate and databricks bundle deploy -t dev directly satisfies the automated development deployment requirement. Microsoft Learn


NEW QUESTION # 18
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.dropna(subset=["order_amount"])
Does this meet the goal?

Answer: A

Explanation:
The correct answer is A - Yes.
df.dropna(subset=['order_amount']) is the idiomatic PySpark way to remove rows where a specific column contains a null. It inspects only the columns listed in subset and drops any row where those columns are null.
The resulting DataFrame contains only rows where order_amount is not null - exactly what the requirement asks for.
The subset parameter is important: without it, dropna() would drop rows where ANY column is null, which could incorrectly exclude rows that have nulls in other columns but a valid order_amount. By specifying subset=['order_amount'], the filter is applied precisely and only to the column in question.
This method is semantically equivalent to df.filter(df.order_amount.isNotNull()) and to the SQL clause WHERE order_amount IS NOT NULL. Both are correct - dropna with a subset is arguably the more readable Pythonic approach.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics


NEW QUESTION # 19
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

Answer: A

Explanation:
To ensure a service principal can query data in two specific catalogs while maintaining the principle of least privilege, you must assign the following permissions for each catalog:
Unified Catalog Permissions
USE CATALOG on the specific catalog: This is a prerequisite that allows the principal to see the catalog and its child objects.
USE SCHEMA on the schemas containing the data: This allows the principal to interact with the schemas within that catalog.
SELECT on the specific tables or views: This provides the actual read access required to query the data.
Reference:
https://learn.microsoft.com/en-us/purview/data-governance-roles-permissions


NEW QUESTION # 20
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to 'All succeeded' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2's 'Depends on' setting in the job configuration.
These two settings are independent and composable. Task1's retry policy gives it multiple chances to recover.
Task2's dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries


NEW QUESTION # 21
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