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2025 Databricks-Certified-Professional-Data-Engineer: Latest Databricks Certified Professional Data Engineer Exam Latest Test Testking

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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.

Databricks Certified Professional Data Engineer is a certification exam offered by Databricks for data engineers. Databricks-Certified-Professional-Data-Engineer Exam evaluates the ability of a candidate to design and implement data solutions using Databricks. Databricks is a unified data analytics platform that enables data teams to collaborate on data engineering, machine learning, and analytics tasks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and proficiency of data engineers in using Databricks for data engineering tasks.

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions 2025 in PDF Format

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?

A. They can institute a retry policy for the entire Job
B. They can observe the task as it runs to try and determine why it is failing
C. They can institute a retry policy for the task that periodically fails
D. They can set up the Job to run multiple times ensuring that at least one will complete
E. They can utilize a Jobs cluster for each of the tasks in the Job

Answer: C

NEW QUESTION # 105
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 ignored.
B. They are deleted.
C. They are merged.
D. They are updated.
E. They are inserted.

Answer: A

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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged." https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20of%20the%20WHEN%20MATCHED%20conditions%20evaluate%20to%20true%20for%20a%20source%20and%20target%20row%20pair%20that%20matches%20the%20merge_condition%2C%20then%20the%20target%20row%20is%20left%20unchanged.

NEW QUESTION # 106
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
E. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.

Answer: E

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 resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 107
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 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.
B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
C. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
D. 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.
E. 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.

Answer: E

Explanation:
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. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 108
The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

A. Delta Lake statistics are not optimized for free text fields with high cardinality.
B. The Delta log creates a term matrix for free text fields to support selective filtering.
C. ZORDER ON review will need to be run to see performance gains.
D. Text data cannot be stored with Delta Lake.
E. Delta Lake statistics are only collected on the first 4 columns in a table.

Answer: A

Explanation:
Explanation
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lakecollects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields.
Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data. References:
Optimizations: https://docs.delta.io/latest/optimizations-oss.html
Full-text search with Elasticsearch: https://docs.databricks.com/data/data-sources/elasticsearch.html Natural language processing: https://docs.databricks.com/applications/nlp/index.html

NEW QUESTION # 109
......

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