Databricks Certified-Data-Engineer-Professional

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

Databricks Certified Data Engineer Professional Sample Questions:

Question #1
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A. /jobs/get
B. /jobs/runs/list
C. /jobs/runs/get-output
D. /jobs/runs/get
E. /jobs/list

Question #2
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
B. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
D. Merge all changes back to the main branch in the remote Git repository and clone the repo again
E. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

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

Question #4
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 is executed only after the full data scan, preventing data skipping.
B. The filter condition involves a data type excluded from data skipping support.
C. The Delta table lacks optimization that enables dynamic file pruning.
D. The event_date column is outside the table's partitioning and Z-ordering scheme.

Question #5
A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
B. Reference each expectation with @dlt.expect decorators in the table declaration.
C. Use a SQL CONSTRAINT block referencing the JSON file path.
D. Invoke an external API to validate records against the metadata rules.

Solutions: