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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
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders. Sales.orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows You need to load the daily data into Sales.orders. The solution must meet the following requirements:
* Preserve the existing data.
* Add only the new records.
* Minimize processing effort.
Which command should include in the loading strategy?
- A. UPDATE
- B. INSERT OVERWRITE
- C. INSERT INTO
Correct Answer: C 🗳️
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out of- memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior. What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Diagnosing OOM errors requires analysing actual runtime execution behaviour. The Spark UI is the primary tool - it ' s accessible from the cluster detail page and captures rich per-stage and per-task metrics without any extra setup.
In the Executors tab, look at storage memory used, execution memory used, memory spill to disk, and GC time per executor. An executor showing high memory spill is a strong indicator that it ' s processing more data than it can hold in memory - often caused by data skew, where one partition is far larger than the others.
The Stages tab shows task distribution - if one task in a stage is processing 10x more data than its peers, that
' s data skew causing memory pressure on that specific executor. Ganglia (available on older runtimes) provides node-level OS metrics like heap usage over time, which can confirm whether memory pressure is sustained or spiky.
These built-in tools give a complete picture of the root cause before making any configuration changes.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/monitor-cluster
You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Set the delta.deletedFileRetentionDuration table property to 10 days
- B. Run the OPTIMIZE command on Table1
- C. Reduce the deleted file retention period to one day.
- D. Run the vacuum command on Table1.
- E. Set the delta. logRetentionDuration table property to 30 days.
Correct Answer: A,D 🗳️
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?
- A. Add an alert and manually restart Job1.
- B. Implement checkpointing.
- C. Decrease the retry interval.
- D. Increase the minimum number of nodes in the cluster
Correct Answer: B 🗳️
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
- A. Add a not null constraint to transactionjd and a check constraint to amount.
- B. Use a select statement with where conditions to validate the data before querying.
- C. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
- D. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
Correct Answer: A 🗳️
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
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