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Databricks Certified Data Engineer Professional Sample Questions:

1. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Other than the default "admins" group, only individual users can be granted privileges on jobs.
B) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
D) Only workspace administrators can grant "Owner" privileges to a group.
E) A user can only transfer job ownership to a group if they are also a member of that group.

2. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
B) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
C) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
D) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.

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

4. A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A) Yes; Delta Lake supports infinite concurrent writers.
B) No; only one stream can write to a Delta Lake table.
C) No; Delta Lake manages streaming checkpoints in the transaction log.
D) Yes; both of the streams can share a single checkpoint directory.
E) No; each of the streams needs to have its own checkpoint directory.

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 filter is executed only after the full data scan, preventing data skipping.
D) The Delta table lacks optimization that enables dynamic file pruning.

Solutions: