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Databricks Certified Data Engineer Professional Sample Questions:

1. An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A) Deep clone the table for the analytics team.
B) Create a new table for the analytics team using a CTAS statement.
C) Shallow clone the table for the analytics team.
D) Give the analytics team direct access to the production table.

2. Which statement characterizes the general programming model used by Spark Structured Streaming?

A) Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
B) Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C) Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
D) Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E) Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

3. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Skew caused by more data being assigned to a subset of spark-partitions.
B) Credential validation errors while pulling data from an external system.
C) Network latency due to some cluster nodes being in different regions from the source data
D) Task queueing resulting from improper thread pool assignment.
E) Spill resulting from attached volume storage being too small.

4. Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A)

B)

C)

D)

5. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
D) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.

Solutions: