Reliable Databricks Certification Databricks-Certified-Professional-Data-Engineer Dumps PDF Jul 17, 2025 Recently Updated Questions [Q59-Q82]

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Reliable Databricks Certification Databricks-Certified-Professional-Data-Engineer Dumps PDF Jul 17, 2025 Recently Updated Questions

Pass Your Databricks Databricks-Certified-Professional-Data-Engineer Exam with Correct 129 Questions and Answers

Databricks-Certified-Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate’s ability to design, implement, and manage data pipelines, as well as leverage advanced analytics and machine learning techniques on the Databricks platform. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and requires candidates to complete a hands-on project that demonstrates their ability to build a data solution on the Databricks platform.

NEW QUESTION 59
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.

A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.

The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

NEW QUESTION 60
Which of the following command can be used to drop a managed delta table and the underlying files in the storage?

DROP TABLE table_name CASCADE

DROP TABLE table_name

Use DROP TABLE table_name command and manually delete files using com-mand dbutils.fs.rm(“/path”,True)

DROP TABLE table_name INCLUDE_FILES

DROP TABLE table and run VACUUM command

NEW QUESTION 61
You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.

Databricks notebooks should be copied to a local machine and setup source control lo-cally to version the notebooks

Databricks notebooks support automatic change tracking and versioning

Databricks Notebooks support real-time coauthoring on a single notebook

Databricks notebooks can be exported into dbc archive files and stored in data lake

Databricks notebook can be exported as HTML and imported at a later time

NEW QUESTION 62
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse.
The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake’s upsert functionality.

Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

NEW QUESTION 63
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
“device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT”
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

withWatermark(“event_time”, “10 minutes”)

awaitArrival(“event_time”, “10 minutes”)

await(“event_time + ’10 minutes'”)

slidingWindow(“event_time”, “10 minutes”)

delayWrite(“event_time”, “10 minutes”)

NEW QUESTION 64
Which of the following is a true statement about the global temporary view?

A global temporary view is available only on the cluster it was created, when the cluster restarts global temporary view is automatically dropped.

A global temporary view is available on all clusters for a given workspace

A global temporary view persists even if the cluster is restarted

A global temporary view is stored in a user database

A global temporary view is automatically dropped after 7 days

NEW QUESTION 65
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?

The total count of rows is calculated by scanning all data files

The total count of rows will be returned from cached results unless REFRESH is run

The total count of records is calculated from the Delta transaction logs

The total count of records is calculated from the parquet file metadata

The total count of records is calculated from the Hive metastore

NEW QUESTION 66
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.

Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

Explanation
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed. This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table12. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
Option A would require applying the churn model to all rows in the customer_churn_params table, which would be wasteful and redundant. It would also require implementing logic to perform an upsert into the predictions table, which would be more complex than using the merge statement.
Option B would require converting the batch job to a Structured Streaming job, which would involve changing the data ingestion and processing logic. It would also require using the complete output mode, which would output the entire result table every time there is a change in the source data, which would be inefficient and costly.
Option C would require calculating the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers, which would be computationally expensive and prone to errors. It would also require storing and accessing the previous predictions, which would add extra storage and I/O costs.
Option D would require modifying the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written, which would add extra complexity and overhead to the data engineering job. It would also require using this field to identify records written on a particular date, which would be less accurate and reliable than using the change data feed.
References: Merge, Change data feed

NEW QUESTION 67
John Smith is a newly joined team member in the Marketing team who currently has access read access to sales tables but does not have access to delete rows from the table, which of the following commands help you accomplish this?

GRANT USAGE ON TABLE table_name TO [email protected]

GRANT DELETE ON TABLE table_name TO [email protected]

GRANT DELETE TO TABLE table_name ON [email protected]

GRANT MODIFY TO TABLE table_name ON [email protected]

GRANT MODIFY ON TABLE table_name TO [email protected]

NEW QUESTION 68
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the “registration” topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

All data should be deleted biweekly; Delta Lake’s time travel functionality should be leveraged to maintain a history of non-PII information.

Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

NEW QUESTION 69
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions

Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.

Appends the data to the current version, you can time travel to previous versions

By default, overwrites the data and schema, you cannot perform time travel

NEW QUESTION 70
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday’s version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = ‘timestamp’

TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)

SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)

SHOW HISTORY table_name AS OF date_sub(current_date(), 1)

NEW QUESTION 71
Which of the following functions can be used to convert JSON string to Struct data type?

TO_STRUCT (json value)

FROM_JSON (json value)

FROM_JSON (json value, schema of json)

CONVERT (json value, schema of json)

CAST (json value as STRUCT)

NEW QUESTION 72
Which statement describes Delta Lake optimized writes?

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.

Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.

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.

Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

NEW QUESTION 73
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.

The write will fail completely because of the constraint violation and no records will be inserted into the target table.

The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.

The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

NEW QUESTION 74
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the “registration” topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

All data should be deleted biweekly; Delta Lake’s time travel functionality should be leveraged to maintain a history of non-PII information.

Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

NEW QUESTION 75
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?

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.

By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

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.

NEW QUESTION 76
Which statement describes integration testing?

Validates interactions between subsystems of your application

Requires an automated testing framework

Requires manual intervention

Validates an application use case

Validates behavior of individual elements of your application

NEW QUESTION 77
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. Theuser_idfield represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table namedaccount_historywhich maintains a full record of all data in the same schema as the source. The next table in the system is namedaccount_currentand is implemented as a Type 1 table representing the most recent value for each uniqueuser_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the describedaccount_currenttable as part of each hourly batch job?

Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.

Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.

Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.

NEW QUESTION 78
What is the main difference between the silver layer and the gold layer in medalion architecture?

Silver may contain aggregated data

Gold may contain aggregated data

Data quality checks are applied in gold

Silver is a copy of bronze data

God is a copy of silver data

NEW QUESTION 79
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

NEW QUESTION 80
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:

Which command allows manual confirmation that these three requirements have been met?

DESCRIBE EXTENDED dev.pii test

DESCRIBE DETAIL dev.pii test

SHOW TBLPROPERTIES dev.pii test

DESCRIBE HISTORY dev.pii test

SHOW TABLES dev

NEW QUESTION 81
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 ofVACUUMcommands 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?

Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

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.

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.

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.

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.

NEW QUESTION 82
A denote the event ‘student is female’ and let B denote the event ‘student is French’. In a class of 100 students
suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I
pick a French student, it will be a girl, that is, find P(A|B).

1/3

2/3

1/6

2/6

The DCPDE exam is a comprehensive assessment that covers a wide range of topics related to data engineering on Databricks. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design, build, and deploy scalable data pipelines on the Databricks platform.

Latest 2025 Realistic Verified Databricks-Certified-Professional-Data-Engineer Dumps: https://www.prepawaypdf.com/Databricks/Databricks-Certified-Professional-Data-Engineer-practice-exam-dumps.html