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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A) The connection to the external table will succeed; the string "redacted" will be printed.
B) An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
C) The connection to the external table will succeed; the string value of password will be printed in plain text.
D) An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
E) The connection to the external table will fail; the string "redacted" will be printed.

2. Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A) Stage's detail screen and Executor's log files
B) Stage's detail screen and Query's detail screen
C) Driver's and Executor's log files
D) Query's detail screen and Job's detail screen
E) Executor's detail screen and Executor's log files

3. A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A) Use broadcast join.
B) Repartition by region before aggregation.
C) Use coalesce() after the aggregation.
D) Caching the DataFrame df.

4. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C) The update throws an error because changes to existing columns in the target schema are not supported.
D) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

5. Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A) The filter condition involves a data type excluded from data skipping support.
B) The event_date column is outside the table's partitioning and Z-ordering scheme.
C) The Delta table lacks optimization that enables dynamic file pruning.
D) The filter is executed only after the full data scan, preventing data skipping.

Solutions: