New Microsoft DP-750 Exam Question & DP-750 Exam Dumps Collection

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Secure and govern Unity Catalog objects15โ€“20%- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials
- Implement data governance and security
  • 1. Enforce data quality, lineage, and auditing
  • 2. Manage catalogs, schemas, tables, views, and volumes
  • 3. Configure access control: row-level, column-level, attribute-based security
Topic 2: Set up and configure an Azure Databricks environment15โ€“20%- 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
- 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
Topic 3: Prepare and process data30โ€“35%- Ingest and transform data
  • 1. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake
- Optimize and manage data storage
  • 1. Implement lakehouse architecture and manage table versions
  • 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 3. Handle structured, semi-structured, and unstructured data
Topic 4: Deploy and maintain data pipelines and workloads30โ€“35%- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Apply SDLC practices and version control
  • 3. Troubleshoot failures, repair and restart jobs
- Build and orchestrate pipelines
  • 1. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 2. Design and implement Lakeflow Spark Declarative Pipelines
  • 3. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs

>> New Microsoft DP-750 Exam Question <<

Top New DP-750 Exam Question | Valid DP-750 Exam Dumps Collection: Implementing Data Engineering Solutions Using Azure Databricks

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q79-Q84):

NEW QUESTION # 79
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You need to configure Cluster1 to meet the following requirements:
- The cluster must scale up automatically when workloads increase.
- The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,D

Explanation:
Enabling autoscaling and setting a 30-minute auto-termination timeout is the correct configuration to meet the goals.
Autoscaling handles fluctuating workloads, while auto-termination prevents you from paying for idle compute resources.
Dynamic Scaling: Autoscaling automatically adds workers during high loads and removes them when demand drops.
Cost Control: The 30-minute termination window ensures the cluster shuts down completely if no jobs are running, stopping all compute charges.
Reference:
https://community.databricks.com/t5/get-started-discussions/cluster-auto-termination-best- practices/td-p/75826


NEW QUESTION # 80
Which feature provides end-to-end tracking of data movement in Databricks?

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 # 81
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 # 82
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog 1. Catalog 1 contains a table named Transactions. Transactions contains the following columns:
* transaction_id
* customet_name
* email address
* credit_card_number
* transaction_amount
You need to ensure that business analysts can query all the tows in the Transactions table. The solution must meet the following requirements:
* Prevent the analysts from seeing the full values in the email_address and credit_catd_number columns.
* Ensure that the analysts can see only the values after the @ character in each email address.
* Ensure that the analysts can see only the last four digits of each credit card number.
* Enable the analysts to query the table without errors.
* Follow the principle of least privilege.
What should you do?

Answer: B

Explanation:
The correct answer is C. Column masks are the right tool when you need to partially expose sensitive data rather than hide it entirely. A column mask is a SQL function attached to a column that rewrites the returned value based on who is querying. For email_address, the mask returns only the substring after '@'. For credit_card_number, it returns only the last four digits. Business analysts get useful data without seeing anything sensitive, and they can still query the table without errors.
Option A (row-level filters) controls which rows a user sees, not the values within a row - it can't partially redact a column. Option B (grant SELECT only on non-sensitive columns) removes the columns entirely, so analysts can't see even partial email or card values - that doesn't meet the 'can see only partial values' requirement. Option D (column-level encryption) requires key management infrastructure and decryption at query time, which is significantly more complex than column masking for this use case.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/row-and-column- filters


NEW QUESTION # 83
What improves join performance for small lookup tables?

Answer: D


NEW QUESTION # 84
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