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Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions – Free Practice Test & Real Exam Prep
Question #1
Although the Databricks Utilities Secrets module provides tools to store sensitive
credentials and avoid accidentally displaying them in plain text users should still be careful
with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?
A. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
B. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
C. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
D. Iterating through a stored secret and printing each character will display secret contents in plain text.
E. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
Answer: E Explanation: This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “List secrets” section.
Question #2
A Databricks SQL dashboard has been configured to monitor the total number of records
present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is
updated?
A. The total count of rows is calculated by scanning all data files
B. The total count of rows will be returned from cached results unless REFRESH is run
C. The total count of records is calculated from the Delta transaction logs
D. The total count of records is calculated from the parquet file metadata E. The total count of records is calculated from the Hive metastore
Which distribution does Databricks support for installing custom Python code packages?
A. sbt
B. CRAN
C. CRAM
D. nom
E. Wheels
F. jars
Answer: D
Question #4
A data architect has heard about lake's built-in versioning and time travel capabilities. For
auditing purposes they have a requirement to maintain a full of all valid street addresses as
they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with
new values and relying on Delta Lake time travel to support long-term auditing. A data
engineer on the project feels that a Type 2 table will provide better performance and
scalability Which piece of information is critical to this decision?
A. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
B. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
D. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires Setting multiple fields in a single update.
Answer: A Explanation: Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for longterm versioning needs, making a Type 2 approach more viable for performance and scalability.References: Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake
Question #5
Which statement describes Delta Lake optimized writes?
A. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
Answer: A Explanation: Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files. References: Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta/optimizations/auto-optimize.html
Question #6
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.files.openCostInBytes
D. spark.sql.adaptive.coalescePartitions.minPartitionNum
E. spark.sql.adaptive.advisoryPartitionSizeInBytes
Answer: A Explanation: This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified References: [Databricks Certified Data Engineer Professional], under “Spark Configuration” section; Databricks Documentation, under “Available Properties - spark.sql.files.maxPartitionBytes” section.
Question #7
Two of the most common data locations on Databricks are the DBFS root storage and
external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
D. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while
mounted directories will always persist saved data to external storage between sessions.
Answer: A Explanation: DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE
Question #8
The view updates represents an incremental batch of all newly ingested data to be
inserted or updated in the customers table.
The following logic is used to process these records.
Which statement describes this implementation?
A. The customers table is implemented as a Type 3 table; old values are maintained as a
new column alongside the current value.
B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
E. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
Answer: A Explanation: The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Merge Into (Delta Lake on Databricks)” section.
Question #9
The data engineering team has configured a job to process customer requests to be
forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta
Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at
1am each Sunday. The total duration of this job is less than one hour. Every Monday at
3am, a batch job executes a series of VACUUM commands on all Delta Lake tables
throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality.
They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses
this concern?
A. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
B. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
C. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
E. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
A data engineer is performing a join operating to combine values from a static userlookup
table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?
A. userLookup.join(streamingDF, ["userid"], how="inner")
B. streamingDF.join(userLookup, ["user_id"], how="outer")
C. streamingDF.join(userLookup, ["user_id”], how="left")
D. streamingDF.join(userLookup, ["userid"], how="inner")
E. userLookup.join(streamingDF, ["user_id"], how="right")
Answer: E Explanation: In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins. References: Structured Streaming Programming Guide: Join Operations Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins
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