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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
- A. Upload the libraries to a Unity Catalog volume and install the libraries on Cluster1.
- B. Install the libraries by using pip from a notebook attached to Cluster1.
- C. Install the libraries on Cluster1 and manually restart the cluster.
- D. Upload the libraries to a Unity Catalog schema and install the libraries on Cluster1.
Correct Answer: A 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
* Ensure that amount is always greater than 0.
* Fail the pipeline update for Table1 when data that violates rule1 is detected.
Which statement should you execute?
- A. @dlt.expect_or_fail( " rule1 " , " amount > 0 " )
- B. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
- C. @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
- D. @dlt.expect( " rule1 " , " amount > 0 " )
Correct Answer: A 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
You have an Azure Databricks workspace.
You have a streaming table named sales_order that is populated by using a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
You need to create a new streaming table named sales_order_by_city that summarizes sales by city and calculates the total sales per city.
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
CREATE OR REFRESH STREAMING TABLE
city
CREATE OR REFRESH STREAMING TABLE defines a streaming table managed by Lakeflow Spark Declarative Pipelines. When the pipeline refreshes, Databricks incrementally processes newly available source data and maintains the resulting table. CREATE OR REPLACE TABLE would create a conventional table and does not provide the required streaming-table semantics. The query selects city AS city and calculates SUM(sales) AS total_sales. Because the aggregation must produce one result for each city, the GROUP BY expression must be city. Neither SUM(sales) nor total_sales belongs in the grouping clause: the former is the aggregate calculation, while the latter is only the alias assigned to its result. This produces continuously maintained city-level sales totals from the sales_order source table.
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-fillna(0, subset=[ ' order_amount ' ])
Does this meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge
