Databricks Certified Data Engineer Professional Exam - Databricks-Certified-Data-Engineer-Professional Exam Practice Test

Question 1

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

A. Choose a compute instance with more disk space.

B. Increase spark.sql.files.maxPartitionBytes.

C. Choose a compute instance with more network bandwidth.

D. Reduce spark.sql.files.maxPartitionBytes.

E. Choose a compute instance with a higher core-to-memory ratio.

Correct Answer: D,E

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Question 2

An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A. Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.

B. Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.

C. Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.

D. Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.

Correct Answer: D

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Question 3

Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

A. OPTIMIZE

B. BUCKETING

C. PARTITION BY

D. COMPACT

E. ANALYZE

Correct Answer: A,E

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Question 4

A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only

B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only

C. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only

D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only

Correct Answer: A

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Question 5

A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A. Use Auto Loader and set cloudFiles.format to "TEXT".

B. Move files to a volume and read with SQL editor.

C. Use Auto Loader and set cloudFiles.format to "BINARYFILE".

D. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").

E. Use Auto Loader and set cloudFiles.format to "IMAGE".

Correct Answer: C,D

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Question 6

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

A. /jobs/list

B. /jobs/runs/get

C. /jobs/get

D. /jobs/runs/get-output

E. /jobs/runs/list

Correct Answer: C

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Question 7

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.

B. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.

C. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

D. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.

Correct Answer: D

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Question 8

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. The email and ltv columns will be returned with the values in user itv.

B. Three columns will be returned, but one column will be named "redacted" and contain only null values.

C. Only the email and itv columns will be returned; the email column will contain all null values.

D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.

E. The email, age. and ltv columns will be returned with the values in user ltv.

Correct Answer: D

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Question 9

What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A. Dynamic data masking is applied only at rest, so it does not affect query performance.

B. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.

C. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

D. PII masking is only required for direct identifiers.

Correct Answer: B

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Question 10

A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

C. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Correct Answer: B

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Question 11

A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Periodically run OPTIMIZE table_name.

B. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

C. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

D. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Correct Answer: A,C

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Question 12

The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When a database is being created, make sure that the LOCATION keyword is used.

B. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

C. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Correct Answer: B

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Question 13

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin

B. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.

C. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.

D. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.

Correct Answer: A

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Question 14

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. Spark cannot capture the topic partition fields from the kafka source.

B. New fields cannot be added to a production Delta table.

C. Updating the table schema requires a default value provided for each file added.

D. Updating the table schema will invalidate the Delta transaction log metadata.

E. New fields will not be computed for historic records.

Correct Answer: E

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Question 15

A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Store only the date value and calculate all time attributes in queries.

B. Store the date as string in ISO format (YYYY-MM-DD) for readability.

C. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)

D. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.

Correct Answer: D

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Databricks Certified Data Engineer Professional - Databricks-Certified-Data-Engineer-Professional Exam Practice Test

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