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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Data transformation and modeling
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Operational reliability
|
| Topic 3: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 4: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
1. You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
2. 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.
3. 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.
4. You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
What should you do?
A) Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
B) Create a volume in the foreign catalog and store curated Delta tables in the volume.
C) Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.
D) In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
5. 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.filter(df.order_amount.isNotNull())
Does this meet the goal?
A) No
B) Yes
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: Only visible for members | Question # 3 Answer: Only visible for members | Question # 4 Answer: D | Question # 5 Answer: B |
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