Databricks Certified Data Engineer Professional Exam Dumps & Practice Test Questions

Question 1:

A batch processing system passes a date parameter to a Databricks notebook via the Databricks Jobs API. The notebook uses this parameter to load data with the following command:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")

Which code snippet correctly defines the Python variable date to be used in this notebook?

A. date = spark.conf.get("date")
B. input_dict = input(); date = input_dict["date"]
C. import sys; date = sys.argv[1]
D. date = dbutils.notebooks.getParam("date")
E. dbutils.widgets.text("date", "null"); date = dbutils.widgets.get("date")

Answer: E

Explanation:

In Databricks, the standard way to pass parameters to notebooks—especially when they are scheduled via the Jobs API—is through widgets. Widgets provide an interactive way to input parameters dynamically at runtime. This is crucial for scenarios such as this, where an upstream system sends a date value that the notebook must retrieve and use.

Looking at the options:

• Option A tries to use spark.conf.get("date"). This method retrieves Spark configuration properties, but job parameters passed by Databricks Jobs are not stored there. Hence, this approach will not correctly fetch the date parameter.

• Option B attempts to use Python’s input() function. This is common in standalone Python scripts for interactive input but is not compatible with Databricks notebooks run as automated jobs. input() would halt execution waiting for manual input, making it unsuitable for scheduled jobs.

• Option C uses sys.argv[1], which is standard for CLI Python scripts receiving arguments. However, Databricks notebooks do not operate through the command line, so this method cannot capture parameters passed by the Jobs API.

• Option D references a method dbutils.notebooks.getParam(), which does not exist in the Databricks Utilities API. This would cause an error if attempted.

• Option E correctly demonstrates the widget approach: first creating a text widget with dbutils.widgets.text("date", "null"), which declares a widget named "date" with a default value of "null". Then it retrieves the actual parameter value with dbutils.widgets.get("date"). This method works seamlessly with parameterized jobs, allowing the notebook to dynamically receive and use the date parameter passed by the upstream system.

This widget-based parameter passing is the officially recommended method by Databricks for integrating notebooks with scheduled jobs or external systems.

Question 2:

In a Databricks workspace, interactive clusters have been set up for different data engineering teams. These clusters are configured to automatically terminate after 30 minutes of inactivity to control costs. Users need to be able to run workloads on their assigned clusters anytime. 

Given that users have been added to the workspace but have not been granted any permissions yet, what is the minimal set of permissions a user must have to start and attach to an existing cluster?

A. "Can Manage" privileges on the cluster
B. Workspace Admin privileges, permission to create clusters, and "Can Attach To" on the cluster
C. Permission to create clusters and "Can Attach To" on the cluster
D. "Can Restart" privileges on the cluster
E. Permission to create clusters and "Can Restart" privileges on the cluster

Answer: D

Explanation:

Managing user permissions in Databricks clusters is essential for balancing cost control and usability. When clusters are preconfigured by an administrator and set to auto-terminate, users need the ability to restart these clusters and attach their workloads without creating unnecessary new clusters or having too much control that could lead to misconfigurations or cost overruns.

Let’s analyze each permission set:

• Option A: "Can Manage" privilege gives users full control over the cluster, including changing settings, restarting, terminating, and attaching. While this covers the required ability to start and attach, it is overly permissive, allowing modifications that might disrupt other users or incur unwanted costs. Hence, it is not the minimal necessary access.

• Option B: Workspace Admin rights, cluster creation, and "Can Attach To" is far too broad. Workspace admins have unrestricted access to the entire environment, which is unnecessary for just running jobs on existing clusters. This excessive permission could lead to security and management issues.

• Option C: Cluster creation allowed and "Can Attach To" allows users to create new clusters and attach to existing ones. However, the scenario specifies users only need to use pre-configured clusters, so cluster creation isn’t required. Additionally, the "Can Attach To" privilege alone doesn’t enable restarting a terminated cluster, which means users may be unable to start clusters once auto-terminated.

• Option D: "Can Restart" privileges is the most precise permission needed. Users can restart clusters that have terminated due to inactivity and inherently can attach to them once running. This permission does not allow changing cluster configurations or terminating the cluster manually, making it the minimal privilege required to meet the business need.

• Option E: Cluster creation and "Can Restart" privileges exceeds the minimal requirements by allowing cluster creation, which is not needed and could cause cost control challenges.

In summary, granting users the "Can Restart" privilege on the cluster enables them to start and attach to the existing clusters while preventing unnecessary cluster management or creation. This approach strikes the right balance between usability and cost control, making option D the best choice.

Question 3:

When setting up Structured Streaming jobs for a production environment, which configuration best ensures automatic recovery from query failures while also minimizing costs?

A. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: Unlimited
B. Cluster: New Job Cluster; Retries: None; Maximum Concurrent Runs: 1
C. Cluster: Existing All-Purpose Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster; Retries: None; Maximum Concurrent Runs: 1

Answer: D

Explanation:

In production environments where Structured Streaming jobs are scheduled, the main goals include maintaining fault tolerance, ensuring job continuity, and managing operational costs effectively. To achieve this, three key configuration aspects must be considered: the type of cluster, retry policy, and the maximum number of concurrent runs.

First, regarding cluster type, a New Job Cluster is preferred because it is created specifically for a job and terminated once the job finishes. This on-demand nature ensures efficient resource use and helps keep costs low, as resources aren’t tied up unnecessarily. On the other hand, an Existing All-Purpose Cluster is designed for multiple interactive or batch jobs sharing resources, which often leads to higher costs and potential resource contention, making it less suitable for dedicated streaming jobs.

Second, the Retries setting is critical for job resilience. Enabling Unlimited Retries allows the system to automatically attempt recovery from transient errors such as network interruptions or brief storage unavailability. Without retries, a failure in the job would cause it to stop immediately, which is unacceptable for production streaming jobs requiring high availability.

Third, the Maximum Concurrent Runs setting controls job parallelism. Restricting this to 1 prevents multiple instances of the same streaming job from running simultaneously, which could cause duplicated data processing or inconsistent results. Allowing unlimited concurrent runs can introduce race conditions and data integrity issues, especially problematic for streaming workloads.

Analyzing the options:

• Option A allows unlimited concurrent runs, risking data integrity.

• Option B has no retries, making the job fragile.

• Option C uses an all-purpose cluster, increasing cost and reducing reliability.

• Option E has neither retries nor the cost benefits of a new cluster.

Thus, Option D strikes the best balance by using a New Job Cluster to control costs, Unlimited Retries for automatic recovery, and limiting to 1 concurrent run to ensure data consistency and job integrity in production.

Question 4:

If an alert triggers notifications for three consecutive minutes and then stops, which statement accurately describes what must have happened?

A. The total average temperature from all sensors exceeded 120 for three consecutive query executions.
B. The recent_sensor_recordings table failed to respond for three consecutive query runs.
C. The source query did not update correctly for three consecutive minutes and then resumed.
D. The maximum temperature from at least one sensor exceeded 120 for three consecutive query executions.
E. The average temperature for at least one sensor exceeded 120 for three consecutive query executions.

Answer: A

Explanation:

This question revolves around interpreting the behavior of an alert system based on a SQL query in Databricks that monitors sensor temperature readings. The query calculates the mean (average) temperature across all sensor data collected in the last five minutes from the recent_sensor_recordings Delta Lake table. The alert condition is triggered if this average temperature exceeds 120 degrees Fahrenheit.

The alert notification triggers every minute if the condition holds true and suppresses further notifications once the condition is no longer met. Given the alert fired notifications for three consecutive minutes, it means that for those three executions of the query, the average temperature across all sensors combined was above 120. Once the notifications stopped, the average dropped back to 120 or below.

Let's evaluate the options:

• A correctly states that the total average temperature across all sensors exceeded 120 for three consecutive checks, which explains the alert behavior.

• B is incorrect because if the table had been unresponsive, the query would fail or return no data, preventing the alert from triggering cleanly. It would not cause consecutive alerts.

• C is unlikely because a failure or improper update of the query would typically result in an error or no alerts rather than a smooth sequence of alert notifications followed by silence.

• D incorrectly focuses on the maximum temperature from a single sensor. The alert is based on the overall mean temperature, not individual maxima, so a spike in one sensor alone is insufficient unless it affects the overall mean.

• E suggests sensor-specific averages, but the query does not group by sensor; it calculates an overall mean across all sensors. Therefore, sensor-specific averages are irrelevant to the alert's trigger condition.

In conclusion, the alert’s behavior directly reflects the total average temperature exceeding the threshold for three consecutive runs, making Option A the only accurate explanation.

Question 5:

How can the developer best access and review the latest code logic in the notebook, specifically from the branch named dev-2.3.9?

A. Create a pull request and use the Databricks REST API to update the current branch to dev-2.3.9.
B. Use the Databricks Repos interface to pull changes from the remote Git repository and switch to the dev-2.3.9 branch.
C. Checkout the dev-2.3.9 branch using Repos and allow automatic conflict resolution with the current branch.
D. Merge all updates into the main branch on the remote repository and reclone the repository.
E. Merge the current branch with dev-2.3.9, then create a pull request to synchronize with the remote repository.

Correct Answer: B

Explanation:

To review the updated notebook logic that resides on the dev-2.3.9 branch, the developer needs to access that specific branch in their local workspace. The key here is to efficiently sync the local environment with the remote repository to see the latest changes without unnecessary complexity.

Option B is the most straightforward and practical approach. Using the Databricks Repos feature, the developer can pull the latest commits from the remote repository and directly switch to the dev-2.3.9 branch. This ensures that the local copy reflects the most recent code updates on the desired branch. It also requires minimal effort and avoids additional overhead.

Option A is less optimal because making a pull request and invoking the REST API introduces unnecessary complexity. The REST API is better suited for automation or scripting, not routine branch switching. Additionally, making a pull request without first checking out the proper branch doesn’t solve the immediate need to review the code.

Option C is risky; while checking out the correct branch is necessary, relying on automatic conflict resolution can unintentionally overwrite or corrupt code. Developers should manually resolve conflicts to ensure accuracy, especially when reviewing critical logic.

Option D is overly complicated. Merging changes back into the main branch and recloning wastes time and resources just to access a particular branch.

Option E involves merging branches and submitting pull requests, which is more suitable for integrating changes rather than simply viewing them.

In summary, pulling the latest changes and selecting the dev-2.3.9 branch through Databricks Repos is the best method for the developer to review the current logic efficiently and safely.

Question 6:

After storing a password in the Databricks secrets module and updating the code to use this secret, what will happen when the code attempts to connect to an external database and print the password variable?

A. The connection will fail, and the output will show the string "REDACTED."
B. An interactive prompt will appear requesting the password; if entered correctly, the connection succeeds and the encoded password saves to DBFS.
C. An interactive prompt will appear requesting the password; if entered correctly, the connection succeeds and the password is printed in plain text.
D. The connection will succeed, and the password will be printed in plain text.
E. The connection will succeed, and the output will show the string "REDACTED."

Correct Answer: E

Explanation:

When using the Databricks secrets module, the primary goal is to securely manage sensitive information like passwords. This module is specifically designed to prevent secrets from being exposed inadvertently in notebook outputs or logs.

In this scenario, the password was initially stored as a plain string in the code, but after uploading it to the secrets store, the code was modified to retrieve the password securely from the secrets module. This ensures that the actual password value is never displayed or logged openly.

Option E is correct because, while the connection to the external database will succeed (assuming all other variables and permissions are correctly configured), the secrets module masks the actual secret value when printed or logged. Instead of showing the real password, it prints "REDACTED" to protect the sensitive information from exposure. This is a security feature to prevent accidental leaks of credentials in shared environments.

Option A is incorrect because using the secrets module properly does not cause connection failures; the connection should work if the credentials and configurations are correct. Also, "REDACTED" appears in output only when printing the secret, not when an error occurs.

Options B and C are incorrect as the secrets module does not prompt users interactively for passwords. It fetches stored secrets programmatically without manual input during execution. Additionally, the module never prints secrets in plain text, ruling out option C.

Option D is incorrect because it contradicts the security design of the secrets module by suggesting the password will be printed plainly. The whole point of using secrets is to avoid that exposure.

In conclusion, the secrets module provides secure secret management by masking sensitive values with "REDACTED" during output while allowing successful authentication and connection behind the scenes. This prevents password leakage in notebooks or logs.

Question 7:

An upstream system writes Parquet files in hourly batches to folders named by the current date. A nightly batch job runs the shown code to ingest all data from the previous day, using a date variable. The combination of customer_id and order_id serves as a composite key uniquely identifying each order. 

Given that the upstream source occasionally produces duplicate records for the same order separated by several hours, which statement accurately describes the behavior of the ingestion process?

A. Each write to the orders table includes only unique records, and only those without duplicates in the target table will be added.

B. Each write contains unique records, but duplicates may already exist in the target table.

C. Each write contains unique records; existing records with the same key will be overwritten.

D. Each write contains unique records; if records with the same key exist, the operation will fail.

E. Each write performs deduplication on both the new and existing records, ensuring no duplicates remain.

Answer: E

Explanation:

When dealing with data ingestion from sources that may produce duplicate entries for the same keys (in this case, composite keys of customer_id and order_id), ensuring the target table contains only unique records is critical for data accuracy and integrity.

Option A suggests that only unique records are written and only if they don’t already exist in the target table. However, this overlooks duplicates within the incoming batch itself. If the batch contains duplicates, those would still be written unless explicitly deduplicated beforehand. This makes A incomplete.

Option B admits new records are unique but ignores the possibility that the target table might already have duplicates. Without handling duplicates across both datasets, this option does not guarantee a duplicate-free final table.

Option C proposes overwriting existing records when the same key is found. While this might update records, it does not handle duplicate records within the new batch or ensure overall uniqueness—it may even cause data loss or unintended overwrites.

Option D implies the process would fail upon encountering duplicate keys in the target table, which is not ideal in batch processing. Instead of failing, the system should gracefully handle duplicates.

Option E correctly describes an approach where deduplication happens across the combined dataset: both the newly ingested records and the existing target data are checked. This ensures that after the merge, the table holds only distinct records. This method is essential when the upstream system might produce duplicate records spaced out in time, as it maintains data integrity and avoids repeated entries.

In summary, deduplication across both incoming and existing data (Option E) is the best practice to prevent duplicate records after ingestion, maintaining a clean, consistent orders table.

Question 8:

Given the following sequence of commands executed in an interactive notebook environment, which statement best describes the outcome?

A. Both commands succeed, and executing show tables will list countries_af and sales_af as views.

B. The first command succeeds. The second command searches all accessible databases for a table or view named countries_af and succeeds if it exists.

C. The first command succeeds, creating a PySpark DataFrame assigned to countries_af. The second command fails because it expects countries_af to be a SQL view.

D. Both commands fail; no variables, tables, or views are created.

E. The first command succeeds, creating a Python list assigned to countries_af. The second command fails.

Answer: C

Explanation:

This question relates to how PySpark DataFrames and SQL views behave in interactive notebooks like Databricks.Command 1 creates a filtered dataset named countries_af from the geo_lookup table for African countries. In PySpark, this command produces a DataFrame object assigned to the variable countries_af. However, this DataFrame is a Python object and not automatically registered as a SQL view accessible via SQL queries.

Command 2 attempts to create a SQL view sales_af by joining the sales table with countries_af. This SQL command expects countries_af to be a registered SQL view or table within the SQL namespace. Since countries_af exists only as a Python variable (a DataFrame) and not as a SQL view, the second command fails because it cannot resolve countries_af in the SQL context.

Now, let's review the options:

• Option A is incorrect because although the first command succeeds, the second command fails due to countries_af not being a SQL view.

• Option B is incorrect because SQL commands don’t automatically search all databases for variables; they operate in the current SQL context. countries_af is not a SQL object.

• Option C accurately describes that the first command succeeds in creating a Python variable holding a DataFrame, but the second command fails since SQL does not recognize it as a view.

• Option D is incorrect because the first command does create a DataFrame, so not both commands fail.

• Option E is incorrect because countries_af is a DataFrame, not a Python list.

In conclusion, the correct understanding is that creating a PySpark DataFrame in Python does not automatically register it as a SQL view, so subsequent SQL commands expecting a SQL view named countries_af will fail. This makes Option C the correct answer.

Question 9:

You have a large dataset stored as Parquet files in a Databricks Delta Lake. Your team wants to optimize query performance for frequent read operations, minimize data storage costs, and ensure data reliability. 

Which of the following strategies should you implement to meet these goals?

A. Use Delta Lake’s OPTIMIZE command with Z-Ordering on frequently queried columns and enable Delta Lake’s automatic data compaction feature.
B. Convert the Parquet files to CSV format and store them in the Delta Lake for faster reads.
C. Disable Delta Lake’s ACID transaction features to reduce overhead and improve query speed.
D. Manually repartition the dataset into smaller files without using Delta Lake’s management features.

Answer: A

Explanation:

In the Databricks Certified Data Engineer Professional exam, a key topic is optimizing performance and storage when working with Delta Lake. Delta Lake is built on top of Apache Spark and adds ACID transactions, scalable metadata handling, and efficient file management to enhance reliability and performance.

Option A is the best choice because OPTIMIZE compacts small files into larger ones, reducing the number of files the query engine has to scan, which improves query speed and reduces overhead. Z-Ordering reorganizes data based on specified columns—commonly used for filtering—so that related data is stored physically close together. This reduces data scan times for frequent queries and enhances cache efficiency. Additionally, Delta Lake supports automatic data compaction, which helps maintain file sizes and keeps performance optimized without manual intervention.

Option B is incorrect because converting Parquet files to CSV reduces efficiency. Parquet is a columnar storage format optimized for fast analytics queries and compression. CSV files are row-based, larger, slower to process, and do not support schema enforcement or predicate pushdown, so switching to CSV will degrade performance and increase storage costs.

Option C is not recommended. Delta Lake’s ACID transaction support is crucial for data reliability, consistency, and concurrent access. Disabling ACID transactions would risk data corruption and inconsistent query results, which violates the principles of reliable data engineering.

Option D—manually repartitioning data into smaller files—can sometimes help but is less effective than leveraging Delta Lake’s built-in features. Manually managing partitions without metadata updates can cause inefficient file sizes, slow queries due to excessive small files (small file problem), and higher storage costs. Delta Lake’s automatic optimization features are designed to solve this at scale.

Thus, leveraging Delta Lake’s OPTIMIZE with Z-Ordering and automatic compaction is the most efficient and reliable strategy to enhance query speed, reduce storage costs, and maintain data integrity for large datasets, aligning perfectly with the best practices covered in the Databricks Certified Data Engineer Professional exam.

Question 10:

You are designing a data pipeline using Databricks to ingest streaming data from Apache Kafka into a Delta Lake table. The data must be processed with exactly-once semantics, and schema evolution is expected as new fields may be added over time. 

Which solution best meets these requirements?

A. Use Structured Streaming with Delta Lake’s merge operation for upserts, enable checkpointing, and configure schema evolution support on the Delta table.
B. Consume Kafka messages using Spark batch jobs scheduled every 5 minutes and overwrite the Delta table each time.
C. Use Databricks Auto Loader with CSV format and append data to a plain Parquet table.
D. Write streaming data directly to S3 and then manually convert files to Delta format once daily.

Answer: A

Explanation:

Ingesting streaming data with exactly-once processing and schema flexibility is a core challenge for data engineers on the Databricks Certified Data Engineer Professional exam.

Option A is the most effective approach. Structured Streaming in Databricks supports reliable, scalable stream processing and can integrate with Delta Lake for stateful operations. Using the merge operation allows you to perform upserts (update or insert) on the Delta table, which is crucial for maintaining exactly-once semantics when events might be reprocessed due to failures or retries. This guarantees that no duplicates or data loss occur. Enabling checkpointing stores the state of the streaming job so it can recover from failures without reprocessing the entire stream. Lastly, enabling schema evolution allows the Delta table to automatically adapt to changes such as new columns without manual intervention, maintaining pipeline stability as source data evolves.

Option B is not ideal because batch jobs introduce latency and cannot guarantee exactly-once semantics in streaming use cases. Overwriting the table every 5 minutes is inefficient, may cause data loss, and complicates concurrency.

Option C is incorrect because Databricks Auto Loader is designed for file ingestion, not streaming from Kafka. Also, using CSV format lacks schema enforcement and performance optimization. Storing data in plain Parquet tables misses the ACID and schema evolution benefits of Delta Lake.

Option D is suboptimal because writing directly to S3 without Delta Lake management means missing ACID guarantees and streaming checkpointing. Manually converting files daily adds operational complexity and delays data availability.

Therefore, Option A aligns with best practices for building robust, scalable, and flexible streaming pipelines on Databricks. It ensures exactly-once processing, fault tolerance, and adaptability to changing data schemas, all critical skills validated in the Databricks Certified Data Engineer Professional exam.