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

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Databricks Certified Data Engineer Professional Sample Questions:

1. Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A) In the Storage Detail screen, by noting which RDDs are not stored on disk
B) In the Executor's log file, by gripping for "predicate push-down"
C) In the Query Detail screen, by interpreting the Physical Plan
D) In the Delta Lake transaction log. by noting the column statistics
E) In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

2. A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A) Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
B) Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
C) Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
D) Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.

3. A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?

A) Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
B) Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
C) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
D) Create a Python wheel file for the entire project, upload it to the Databricks Workspace Files or Volumes, and install it using a Cluster Library or pip install in a requirements.txt declared within a Databricks Asset Bundle.

4. A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

A) Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
B) Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.
C) Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.
D) Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.

5. 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) Shuffle Hash Joins are always more efficient than Sort Merge Joins.
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) The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Solutions: