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

Databricks Certified Data Engineer Professional Sample Questions:

1. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D) Databricks has autotuned to a smaller target file size based on the amount of data in each partition

2. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
B) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
C) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
D) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.

3. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
B) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
D) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
E) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

4. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
C) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

5. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

Solutions: