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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
- Deleted data must be retained for 30 days to support audits.
- Deleted data that is older than 30 days must be removed permanently.
- The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) delta.timeUntilArchived
B) delta.enableDeletionVectors
C) delta.deletedFileRetentionDuration
D) delta.logRetentionDuration
E) delta.autoOptimize.autoCompact

2. You need to ingest real-time IoT data into Delta Lake with exactly-once guarantees. Which approach should you use?

A) Manual ingestion using notebooks
B) Structured Streaming with checkpointing
C) Batch ingestion using ADF
D) Copy activity with retry policy

3. 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.
- Prevent an update to Table1 from being committed when data that
violates rule1 is detected.
Which statement should you execute?

A) @dlt.expect_or_drop("rule1", "amount > 0")
B) @dlt.expect_or_fail("rule1", "amount > 0")
C) @dlt.expect_all_or_drop({"rule1": "amount > 0"})
D) @dlt.expect("rule1", "amount > 0")

4. What happens if incoming data violates Delta table schema?

A) Data is rejected
B) Data is automatically cast
C) Data is appended with nulls
D) Table is overwritten

5. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?

A) Values appear in Table2 but NOT Table1.
B) Values appear in both tables more than once.
C) Values appear in Table1 more than once.
D) Values appear in either table more than once.

Solutions: