Databricks Data Engineer Professional Exam Dumps & Questions 2025
Databricks Data Engineer Professional Exam Dumps & Questions 2025
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Question 1:
The data engineering team is facing issues with cloud storage costs. The cloud team informed the engineering team about the historic data stored for transactions Delta table. The data engineering team wants to get rid of the data files older than 60 hours. One of the members suggested using VACUUM command to remove the data completely but the team wants to check the list of files that will be deleted after running the VACUUM command. Which of the following commands can be used for that?
A. VACUUM transactions 2.5 DRY RUN
B. VACUUM transactions 60 DRY_RUN
C. VACUUM transactions 2.5 DRY_RUN
D. VACUUM transactions 60 DRY RUN
E. VACUUM transactions 2.5 DAYS DO_NOT_DELETE
Answer: A
Explanation:
✅ VACUUM transactions 60 DRY RUN
VACUUM command: This is the correct command for removing older data files from a Delta table.
transactions: This specifies the Delta table name.
60: This specifies the retention interval in hours (as implied by the context of “60 hours”).
DRY RUN: This crucial part of the command allows you to preview the files that would be deleted without actually deleting them. This is exactly what the team wants.
This is the correct syntax, and the correct time unit.
❌ VACUUM transactions 2.5 DRY RUN
While DRY RUN is correct, 2.5 is not the correct amount of hours. The question specified 60 hours.
The time amount is incorrect.
❌ VACUUM transactions 2.5 DRY_RUN
The underscore in DRY_RUN will cause the command to fail. DRY RUN is the correct syntax.
Also, the time amount is incorrect.
❌ VACUUM transactions 60 DRY_RUN
The underscore in DRY_RUN will cause the command to fail. DRY RUN is the correct syntax.
The syntax of the dry run is incorrect.
❌ VACUUM transactions 2.5 DAYS DO_NOT_DELETE
The time unit is incorrect, and the DO_NOT_DELETE is not a valid parameter.
The command does not support days, and also the do not delete portion of the command is invalid.
Question 2:
When mounting an external data source in Databricks, which of the following options provides the best approach for managing secrets, such as access keys or credentials, required to access the external storage?
A. Storing secrets directly in the Databricks notebook code.
B. Embedding secrets in environment variables within the Databricks cluster configuration.
C. Including secrets in plain text files within the mount options when defining the mount point.
D. Storing secrets in Databricks Secrets.
E. Using a third-party password manager to securely store and retrieve the secrets.
Answer: D
Explanation:
Best Practices for Securely Handling Secrets in Databricks
When mounting an external data source in Databricks, it is essential to handle secrets—such as access keys or credentials—securely. Below, we evaluate different approaches to determine the most secure option.
1. Storing Secrets Directly in Databricks Notebook Code
Storing secrets within notebook code poses a significant security risk, as they may be exposed in logs or inadvertently shared with others. This approach is not recommended.
2. Embedding Secrets in Environment Variables within the Databricks Cluster Configuration
Similar to storing secrets in notebook code, embedding secrets in environment variables can also introduce security vulnerabilities. These secrets may be exposed in logs or accessible to unintended users, making this method insecure.
3. Including Secrets in Plain Text Files within Mount Options
Defining mount options with secrets in plain text files is not recommended by Databricks. This method can expose sensitive information, leading to potential security breaches.
4. Storing Secrets in Databricks Secrets ✅ (Recommended)
Databricks Secrets provides a secure, built-in key-value store designed for managing secrets. It encrypts stored values both at rest and in transit, ensuring secure access within notebooks or jobs using the Databricks Secrets API. This approach is the most secure and strongly recommended.
5. Using a Third-Party Password Manager
While third-party password managers can securely store and retrieve secrets, Databricks Secrets is a native feature that offers a seamless and integrated solution for managing credentials directly within the Databricks environment.
Question 3:
A Databricks user needs to cancel the run of a job but does not have the access to the REST API or the UI. The only access provided to the user is Databricks CLI. Which of the following commands can be used by the user to cancel the run of a job with the following details:
job-id – 2795
run-id – 96746
job-name – fetch_details
A. databricks run cancel --run-id 2795
B. databricks runs cancel --job-id 2795 --run-id 96746
C. databricks run cancel --job-name fetch_details --run-id 96746
D. databricks runs cancel --run-id 96746
E. databricks run cancel --job-id 2795 --run-id 96746
Answer: E
Explanation:
Understanding Job Identifiers in Databricks
In Databricks, each job has a unique job ID, and every execution of a job is assigned a distinct run ID.
If you need to cancel a job run, you must use the run ID associated with that specific execution.
The correct command to cancel a job run is:
databricks runs cancel --run-id 96746
Job names are not unique identifiers, as multiple jobs can share the same name. Therefore, using a job name to identify or cancel a job is not reliable.
This ensures precise job management and prevents accidental cancellations of unintended job executions.
Question 4:
A data engineer is assigned the task of creating a table using a CSV file stored in local storage. The data engineer executes the following SQL statement and the table is created successfully.
CREATE TABLE venues
(name STRING, area INT)
USING CSV
LOCATION ‘dbfs:/FileStore/data/venues.csv‘
Now, the data engineer tries to add a record to the table using INSERT INTO command. Which of the following would be the output of the INSERT INTO command?
A.The record will be inserted in the venues table and a new CSV file will be added in dbfs:/FileStore/data/ directory.
B. The record will not be inserted in the table and an error message will be displayed.
C. The record will be inserted in the table as well as the venues.csv file.
D. The record will not be inserted in the table but an OK message will be displayed.
E. The record will be inserted in the venues.csv file but not in the venues table.
Answer: B
Explanation:
Handling Data Insertion in Databricks Tables
When attempting to insert data into the venues table, you may encounter the following error:
AnalysisException: Cannot insert into dbfs:/FileStore/data/venues.csv, as it is a file instead of a directory.
This occurs because the venues table is created with dbfs:/FileStore/data/venues.csv as its LOCATION, meaning it references a single file rather than a directory. As a result, new records cannot be inserted into the table.
To enable data insertion:
The table should be created with dbfs:/FileStore/data/ as the LOCATION instead of referencing a specific file.
This allows new records to be added, and a new CSV file will be created within the dbfs:/FileStore/data/ directory for each insertion.
This approach ensures proper data management and seamless record insertion in Databricks.
Question 5:
A Kafka stream that acts as an upstream system in an ETL framework tends to produce duplicate values within a batch. The streaming query reads the data from the source and writes to the downstream delta table using the default trigger interval. If the upstream system emits the data every 20 minutes, which of the following strategies can be used to remove the duplicates before saving the data to the downstream delta table while keeping the costs low?
A. Use dropDuplicates method after every 20 minutes on the target table.
B. Change the downstream table to a temporary table in the streaming query, drop the duplicates from the temporary table every 20 minutes, and load the data to the original downstream table.
C. Update the processing time to 20 minutes and add dropDuplicates() in the streaming query.
D. Adding dropDuplicates() to the streaming query will remove duplicate values from all previous batches of data.
E. Add withWatermark method in the streaming query with 20 minutes as the argument.
Answer: C
Explanation:
A. Use dropDuplicates() method every 20 minutes on the target table.
❌ Incorrect!
While this approach can remove duplicates, it is not cost-effective in a streaming query, as repeatedly applying dropDuplicates() on the downstream table every 20 minutes can lead to unnecessary processing overhead.
B. Use a temporary table in the streaming query, drop duplicates every 20 minutes, and load data into the original table.
❌ Incorrect!
Although this method can work, using a temporary table increases storage costs unnecessarily, making it an inefficient solution.
C. Update the processing time to 20 minutes and add dropDuplicates() in the streaming query.
✅ Correct!
By changing the processing time from the default 500 ms to 20 minutes, all duplicate records within a batch can be efficiently removed using the dropDuplicates() method. This ensures that duplicates within a batch are handled properly without incurring additional costs.
D. Add dropDuplicates() to the streaming query to remove duplicates from all previous batches.
❌ Incorrect!
The dropDuplicates() method only removes duplicates within the current batch and does not affect previous batches. To manage duplicates across multiple batches, additional logic is required.
E. Add withWatermark() in the streaming query with a 20-minute threshold.
❌ Incorrect!
The withWatermark() function requires a time column in the streaming source. Additionally, dropDuplicates() must still be explicitly added to remove duplicate records within the specified 20-minute timeframe.
For a full set of 615 questions. Go to
https://skillcertpro.com/product/databricks-data-engineer-professional-practice-tests/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.
Question 6:
Managing Long-running Streaming Queries‘ State Size For a Spark Structured Streaming application with stateful processing that grows indefinitely over time, how can you manage the state size to prevent resource exhaustion?
A.Implement state imeout logic using mapGroupsWithState or flatMapGroupsWithState and specify a timeout duration to purge old state data
B.Regularly checkpoint the streaming state to an external durable store and manually truncate the state store at intervals
C.Use the state operator to explicitly define state storage level as MEMORY_ONLY_SER, forcing old state data to be serialized and stored on disk
D.Configure the streaming query to restart periodically, thereby resetting the state store and preventing unbounded growth
Answer: A
Explanation:
Implementing State Timeout Logic in Spark Structured Streaming
🔹 Key Strategy: Implement state timeout logic using mapGroupsWithState or flatMapGroupsWithState and specify a timeout duration to automatically purge old state data.
1. Managing Stateful Processing Efficiently
mapGroupsWithState and flatMapGroupsWithState enable custom state management for streaming queries.
By specifying a timeout duration, old state data is automatically purged, preventing excessive growth in the state store.
2. Preventing Resource Exhaustion
If state data is not purged, it continues growing indefinitely, consuming excessive memory and storage.
State timeout logic ensures that old, unused state entries are removed periodically, maintaining system performance.
3. Efficiency Compared to Other Methods
✅ Automated state cleanup eliminates the need for manual interventions such as:
Manually checkpointing state (which adds overhead).
Restarting the streaming query periodically (which can be disruptive).
Using external storage solutions (which may introduce latency).
4. Scalability for Large Streaming Workloads
Allows Spark to handle large volumes of stateful data efficiently.
Prevents performance degradation over time.
Ensures optimal memory usage while maintaining the integrity of stateful processing.
Question 7:
Cross-Version Compatibility Testing for Databricks Notebooks: With the release of new Databricks Runtime versions, you need to ensure that existing notebooks remain compatible and performant. What testing strategy ensures compatibility across runtime versions?
A.Setting up parallel environments in Azure Databricks, each running a different runtime version, and executing all notebooks to compare outputs and performance
B.Utilizing Databricks Jobs API to programmatically run notebooks against multiple runtime versions, analyzing logs for errors or performance degradation
C.Manually updating a test environment to new runtime versions as they are released, running a set of benchmark notebooks, and documenting any issues
D.Implementing continuous integration workflows that automatically test notebook compatibility with new runtime versions using Azure DevOps pipelines
Answer: B
Explanation:
Option B is the most suitable approach for ensuring cross-version compatibility testing for Databricks notebooks. Below is a detailed explanation of why this option is the best choice:
Automation:
Leveraging the Databricks Jobs API enables the automation of the testing process. This allows you to execute the same set of notebooks across multiple runtime versions without manual intervention, significantly reducing time and effort.
Scalability:
Programmatically running notebooks against multiple runtime versions ensures scalability. You can efficiently test a large number of notebooks across various versions without the need for manual execution, making the process more manageable and consistent.
Error Detection:
By analyzing logs generated during notebook execution, you can quickly identify errors or performance degradation that may arise when running on different runtime versions. This facilitates efficient troubleshooting and resolution of compatibility issues.
Efficiency:
This approach is more efficient than setting up parallel environments or manually updating test environments. It provides a streamlined and systematic testing process that can be seamlessly integrated into existing testing workflows.
Documentation:
Analyzing logs for errors or performance issues allows you to document any problems encountered during testing. This documentation serves as a valuable resource for tracking notebook compatibility across different runtime versions and informing future testing efforts.
Question 8:
What Delta Lake optimization technique is most effective in Databricks for improving query performance on large datasets stored in Azure Data Lake Storage Gen2?
A.Implementing Z-order optimization on frequently queried columns
B.Storing data in a single large file to reduce the number of read operations
C.Using Azure Redis Cache to store intermediate query results
D.Enabling Azure CDN for Delta Lake files to increase data retrieval speed
Answer: A
Explanation:
Z-order optimization is a technique used to physically reorganize data in a Delta Lake table based on the values of one or more columns. By clustering related data together in the same physical location on disk, Z-order optimization can significantly improve query performance by reducing the amount of data that needs to be scanned during query execution.
In the context of large datasets stored in Azure Data Lake Storage Gen2, implementing Z-order optimization on frequently queried columns can be highly effective in enhancing query performance. By organizing the data based on the values of columns that are commonly used in queries, Z-order optimization minimizes the amount of data that needs to be read from disk, resulting in faster query execution times.
Comparison with Other Options:
Option B (Storing Data in a Single Large File): This approach may not necessarily improve query performance and could even increase the amount of data that needs to be read for each query.
Option C (Using Azure Redis Cache to Store Intermediate Query Results): While this may help with caching and speeding up subsequent queries, it does not address the root cause of slow query performance.
Option D (Enabling Azure CDN for Delta Lake Files): This can improve data retrieval speed for remote clients but may not have a direct impact on query performance within Databricks.
Question 9:
What is the correct method to query a Delta Lake table using Spark SQL directly?
A.spark.sql(“SELECT * FROM delta./path/to/table“)
B.spark.read.format(“delta“).load(“/path/to/table“).show()
C.spark.sql(“SELECT * FROM delta_table“).show()
D.spark.read.delta(“/path/to/table“).sql(“SELECT * FROM table“)
Answer: B
Explanation:
The correct method to query a Delta Lake table using Spark SQL directly is to use the spark.read.format(“delta”).load(“/path/to/table”) function. This function reads the data from the specified Delta Lake table and loads it into a DataFrame, which can then be used to perform SQL queries using Spark SQL. The .show() function is used to display the results of the query.
Incorrect Options:
Option A: The syntax “SELECT * FROM delta./path/to/table” is not valid for querying a Delta Lake table directly using Spark SQL.
Option C: The syntax “SELECT * FROM delta_table” is not valid for querying a Delta Lake table directly using Spark SQL. The correct way to reference a Delta Lake table in Spark SQL is by specifying the path to the table.
Option D: The syntax “spark.read.delta(“/path/to/table”).sql(“SELECT * FROM table”)” is not valid for querying a Delta Lake table directly using Spark SQL.
Question 10:
Which Databricks CLI command correctly imports a notebook into a Databricks workspace and sets its language?
A.databricks workspace import -l PYTHON /LocalPath/Notebook.py /WorkspacePath/Notebook
B.databricks workspace import --language PYTHON --source /LocalPath/Notebook.py --target /WorkspacePath/Notebook
C.databricks fs cp /LocalPath/Notebook.py /WorkspacePath/Notebook.py --language PYTHON
D.databricks workspace import /LocalPath/Notebook.py /WorkspacePath/Notebook.py --language PYTHON
Answer: B
Explanation:
This command is the correct method for importing a notebook into a Databricks workspace and setting its language. Below is a detailed breakdown of each part of the command:
databricks workspace import:
This is the main command that instructs the Databricks CLI to import a file into the workspace.
–language PYTHON:
This flag specifies the language of the notebook being imported. In this case, it is set to Python, ensuring that the notebook is correctly identified and executed as a Python notebook within the Databricks workspace.
–source /LocalPath/Notebook.py:
This flag specifies the source file path of the notebook being imported. You must provide the full path to the notebook file on your local machine.
–target /WorkspacePath/Notebook:
This flag specifies the target path within the Databricks workspace where the notebook will be imported. You need to provide the full path to the desired location in the workspace.
Why This Command is Correct:
By using this command, you not only import the notebook into the Databricks workspace but also explicitly set its language to Python. This ensures that the notebook is correctly recognized and executed as a Python notebook within the workspace.
For a full set of 615 questions. Go to
https://skillcertpro.com/product/databricks-data-engineer-professional-practice-tests/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.