Pass Your Databricks Databricks-Certified-Data-Engineer-Professional Exam with Correct 127 Questions and Answers
Latest [Nov 09, 2025] 2025 Realistic Verified Databricks-Certified-Data-Engineer-Professional Dumps
NEW QUESTION # 47
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?
- A. 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.
- B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
- C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
- 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 finishes.
- E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
Answer: B
Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.
NEW QUESTION # 48
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?
- A. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
- C. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
- D. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
- E. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
Answer: A
Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.
NEW QUESTION # 49
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
- A. Yes; Delta Lake supports infinite concurrent writers.
- B. No; only one stream can write to a Delta Lake table.
- C. Yes; both of the streams can share a single checkpoint directory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from - D. No; Delta Lake manages streaming checkpoints in the transaction log.
- E. No; each of the streams needs to have its own checkpoint directory.
Answer: E
Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss.
NEW QUESTION # 50
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).
Which statement describes what will happen when the above code is executed?
- A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
- B. The connection to the external table will succeed; the string "redacted" will be printed.
- C. The connection to the external table will fail; the string "redacted" will be printed.
- D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
- E. The connection to the external table will succeed; the string value of password will be printed in plain text.
Answer: B
Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.
NEW QUESTION # 51
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?
- A. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
- B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
- C. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
- D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
- E. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
Answer: B
Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.
NEW QUESTION # 52
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?
- A. date = spark.conf.get("date")
- B. date = dbutils.notebooks.getParam("date")
- C. input_dict = input()
date= input_dict["date"] - D. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date") - E. import sys
date = sys.argv[1]
Answer: D
Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter.
For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
NEW QUESTION # 53
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from made based upon what is most convenient for the workspace administrator.
- B. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- C. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- E. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
Answer: D
Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.
NEW QUESTION # 54
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?
- A. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
- B. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
- C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
- D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
- E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
Answer: D
Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.
NEW QUESTION # 55
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
- A. Can manage
- B. Can edit
- C. Can run
- D. Can Read
Answer: D
Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.
NEW QUESTION # 56
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 deleted.
- B. They are updated.
- C. They are merged.
- D. They are inserted.
- E. They are ignored.
Answer: E
Explanation:
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events.
NEW QUESTION # 57
A Delta Lake table was created with the below query:
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Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
- A. A new Delta transaction log Is created for the renamed table.
- B. The table name change is recorded in the Delta transaction log.
- C. The table reference in the metastore is updated and all data files are moved.
- D. The table reference in the metastore is updated and no data is changed.
- E. All related files and metadata are dropped and recreated in a single ACID transaction.
Answer: D
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.
NEW QUESTION # 58
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?
- A. SHOW TABLES dev
- B. DESCRIBE EXTENDED dev.pii test
- C. DESCRIBE DETAIL dev.pii test
- D. DESCRIBE HISTORY dev.pii test
- E. SHOW TBLPROPERTIES dev.pii test
Answer: B
Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.
NEW QUESTION # 59
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 executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
- B. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
- C. 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.
- D. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
- E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
Answer: A
Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
NEW QUESTION # 60
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Which statement describes what the number alongside this field represents?
- A. The number of times the job definition has been run in the workspace.
- B. The job_id is returned in this field.
- C. The job_id and number of times the job has been are concatenated and returned.
- D. The total number of jobs that have been run in the workspace.
- E. The globally unique ID of the newly triggered run.
Answer: E
Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.
NEW QUESTION # 61
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.
Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?
- A. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
- B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
- C. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
- D. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
- E. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
Answer: B
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
NEW QUESTION # 62
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
- B. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- C. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
- D. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- E. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
Answer: B
Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset
NEW QUESTION # 63
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?
- A. Integration
- B. functional
- C. End-to-end
- D. Manual
- E. Unit
Answer: E
Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
NEW QUESTION # 64
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
- A. Task queueing resulting from improper thread pool assignment.
- B. Skew caused by more data being assigned to a subset of spark-partitions.
- C. Network latency due to some cluster nodes being in different regions from the source data
- D. Spill resulting from attached volume storage being too small.
- E. Credential validation errors while pulling data from an external system.
Answer: B
Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.
NEW QUESTION # 65
The following table consists of items found in user carts within an e-commerce website.
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The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.
How would the following update be handled?
- A. The update throws an error because changes to existing columns in the target schema are not supported.
- B. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
- C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
- D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
Answer: B
Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
NEW QUESTION # 66
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:
For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:
Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?
- A. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
- B. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
- C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
- D. Define a view against the products_per_order table and define the dashboard against this view.
- E. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
Answer: A
NEW QUESTION # 67
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. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
- B. 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.
- C. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
- 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. 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.
Answer: D
Explanation:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types.
NEW QUESTION # 68
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