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Data Engineer Professional — Databricks Certified Data Engineer Professional Study Guide

230 practice questions Updated 2026-04-11 $19 (70% off) HTML + PDF formats

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Question 79

Which statement regarding Spark configuration on the Databricks platform is true?

A. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs currently running on the cluster.
B. Spark configurations set within a notebook will affect all SparkSessions attached to the same interactive cluster.
C. When the same Spark configuration property is set for an interactive cluster and a notebook attached to that cluster, the notebook setting will always be ignored.
D. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
Show Answer
Correct Answer: D
Explanation:
Spark configuration properties defined at the interactive cluster level via the Databricks Clusters UI are applied to the cluster itself and therefore affect all notebooks attached to that cluster. Notebook-level Spark configs are session-scoped and do not override cluster-wide defaults universally, and cluster configs generally require a restart to change.

Question 72

A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic: A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67. Which statement describes the outcome of this batch insert?

A. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
D. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
Show Answer
Correct Answer: B
Explanation:
In Delta Lake, CHECK constraints are enforced atomically on write operations. If any record in a batch violates a CHECK constraint (such as an invalid longitude value of 212.67), the entire write operation fails and no records from that batch are committed to the table.

Question 43

A Data Engineer wants to run unit tests using common Python testing frameworks on Python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against functions that work with data in production?

A. Define and import unit test functions from a separate Databricks notebook
B. Define and unit test functions using Files in Repos
C. Run unit tests against non-production data that closely mirrors production
D. Define unit tests and functions within the same notebook
Show Answer
Correct Answer: B
Explanation:
To run unit tests with standard Python testing frameworks (such as pytest or unittest), the functions must be importable as regular Python modules. Storing production functions in Files in Repos (as .py files) allows them to be imported both into Databricks notebooks and into test files, enabling proper unit testing and CI/CD workflows. The other options either do not address testability with common frameworks or describe data management practices rather than how to structure and execute unit tests.

Question 204

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 to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications. The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. The 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.
B. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
C. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
D. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
E. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
Show Answer
Correct Answer: D
Explanation:
Databricks schema inference chooses permissive data types that can accommodate all observed values, which may weaken type guarantees for complex, nested data. Manually declaring schema enforces precise types and constraints, providing stronger data quality enforcement for production dashboards and models. The other options make incorrect claims about Tungsten/JSON efficiency, Parquet mutability, labor costs as a universal priority, or guarantees of inference accuracy.

Question 103

What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Run source env/bin/activate in a notebook setup script
B. Install libraries from PyPI using the cluster UI
C. Use %pip install in a notebook cell
D. Use %sh pip install in a notebook cell
Show Answer
Correct Answer: C
Explanation:
Using %pip install in a notebook cell installs the package in a notebook-scoped environment that is propagated to all nodes of the currently active cluster for that notebook session. Cluster UI installs affect all notebooks, %sh pip install runs only on the driver, and activating a virtual environment is not applicable in this context.

Question 182

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 triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
B. 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.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
Show Answer
Correct Answer: E
Explanation:
In Databricks, the %sh magic runs shell commands only on the driver node. As a result, cloning repositories, running scripts, and moving data via %sh do not leverage Spark’s distributed execution or worker nodes. Processing ~1 GB of data on a single driver can therefore be much slower than using Spark or Databricks-native APIs that parallelize work across the cluster.

Question 81

A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A. Use Repos to create a new branch, commit all changes, and push changes to the remote Git repository.
B. Use Repos to create a fork of the remote repository, commit all changes, and make a pull request on the source repository.
C. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
D. Use Repos to merge all differences and make a pull request back to the remote repository.
Show Answer
Correct Answer: A
Explanation:
Since the user cannot commit directly to the protected main branch, the safe and supported workflow in Databricks Repos is to create a new branch, commit their changes there, and push that branch to the remote repository. This allows teammates to review and merge the changes via the normal Git process without risking overwriting work on main.

Question 168

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A. If task A fails during a scheduled run, which statement describes the results of this run?

A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
Show Answer
Correct Answer: D
Explanation:
In Databricks Jobs, task dependencies are enforced: if a parent task fails, all dependent tasks are skipped. There is no automatic transactional rollback across tasks or across an entire notebook. Since Task A is a notebook, any operations that successfully committed (for example, Delta table writes) before the failure remain committed. Therefore, Tasks B and C are skipped, and some logic in Task A may already have been committed before it failed.

Question 179

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used. Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
D. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
Show Answer
Correct Answer: A
Explanation:
The requirement is to achieve ~512 MB Parquet files with the best performance *without shuffling*. Any use of repartition, sort, or shuffle-related settings violates that constraint. Option A works because spark.sql.files.maxPartitionBytes controls input split size when reading file-based sources like JSON. With only narrow transformations, Spark preserves the input partitioning through to the write, so the number and size of output Parquet files closely follow the read partitions. All other options either explicitly trigger a shuffle (repartition, sort) or rely on shuffle-only mechanisms (shuffle partitions, AQE advisory size). Therefore, A is the only strategy that aligns with the no-shuffle requirement and provides the best performance.

Question 109

A view is registered with the following code: Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
B. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
Show Answer
Correct Answer: D
Explanation:
In Databricks, a standard SQL view is not materialized or cached by default. Its logic is re-evaluated every time the view is queried. Because Delta Lake provides snapshot isolation, the query operates on a consistent snapshot of the source tables taken at the start of the query, so changes committed while the query is running are not reflected. Therefore, the view returns the result of joining the valid versions of the source tables as of when the query began.

Question 201

A junior data engineer on your team has implemented the following code block. The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table. When this query is executed, what will happen with new records that have the same event_id as an existing record?

A. They are merged.
B. They are ignored.
C. They are updated.
D. They are inserted.
E. They are deleted.
Show Answer
Correct Answer: B
Explanation:
In a Delta MERGE operation, if rows match on the merge condition (same event_id) but there is no WHEN MATCHED clause specified, no action is taken on those matched rows. Therefore, new records with an existing event_id are left unchanged (ignored).

Question 122

When using CLI or REST API to get results from jobs with multiple tasks, which statement correctly describes the response structure?

A. Each run of a job will have a unique job_id; all tasks within this job will have a unique job_id
B. Each run of a job will have a unique job_id; all tasks within this job will have a unique task_id
C. Each run of a job will have a unique orchestration_id; all tasks within this job will have a unique run_id
D. Each run of a job will have a unique run_id; all tasks within this job will have a unique task_id
E. Each run of a job will have a unique run_id; all tasks within this job will also have a unique run_id
Show Answer
Correct Answer: E
Explanation:
In Databricks Jobs (CLI/REST) for multi-task jobs, the job definition has a job_id, each execution of the job has a top-level run_id, and each task execution within that job run also has its own distinct run_id. The API response (e.g., jobs/runs/get) shows a parent run_id and a tasks array where each task includes its own run_id, which is used to fetch task-specific outputs.

Question 217

A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. The five Minute Load Average remains consistent/flat
B. Bytes Received never exceeds 80 million bytes per second
C. Total Disk Space remains constant
D. Network I/O never spikes
E. Overall cluster CPU utilization is around 25%
Show Answer
Correct Answer: E
Explanation:
With 3 executors plus 1 driver using identical VM types, if code is executing primarily on the driver (e.g., collect(), driver-side processing), the driver’s CPU may be near 100% while executors are mostly idle. Averaged across the cluster, Ganglia would show ~25% overall CPU utilization (1 of 4 nodes busy), which is a clear signal of a driver-side bottleneck.

Question 76

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.files.maxPartitionBytes
B. spark.sql.autoBroadcastJoinThreshold
C. spark.sql.adaptive.advisoryPartitionSizeInBytes
D. spark.sql.adaptive.coalescePartitions.minPartitionNum
Show Answer
Correct Answer: A
Explanation:
The parameter that directly controls the size of Spark partitions at data ingestion time is spark.sql.files.maxPartitionBytes. It defines the maximum number of bytes packed into a single partition when reading file-based data sources (e.g., Parquet, JSON, ORC). The other options relate to join optimization or adaptive query execution after data has already been read, not initial partition sizing.

Question 213

The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE". The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day. Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.mode("append").saveAsTable("churn_preds")
B. preds.write.format("delta").save("/preds/churn_preds")
C.
D.
E.
Show Answer
Correct Answer: A
Explanation:
Predictions are generated as a batch job at most once per day and must be retained over time for historical comparison. Using `mode("append")` ensures new daily predictions are added without overwriting prior results. `saveAsTable` creates a managed Delta table by default in Databricks, so Delta Lake capabilities are preserved without extra configuration, minimizing complexity and compute costs.

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