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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A) The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
B) The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
C) Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
D) Shuffle Hash Joins are always more efficient than Sort Merge Joins.

2. A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A) Joining on event time constraint: clickTime == impressionTime using a leftOuter join
B) Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
C) Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
D) Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks

3. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
B) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
C) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
D) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
E) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

4. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
B) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
C) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
E) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

5. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A) Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
B) By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
C) Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D) Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

Solutions: