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Databricks Certified Professional Data Engineer certification exam is a hands-on exam that requires candidates to demonstrate their skills in building data pipelines and workflows using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of a set of performance-based tasks that require candidates to design, implement, and manage data solutions in a Databricks environment. Candidates are given a set of data engineering scenarios and must use Databricks to build solutions that meet the requirements of each scenario.

Databricks is a cloud-based data engineering platform that allows organizations to process large amounts of data quickly and efficiently. The platform leverages Apache Spark to perform data processing tasks and offers a wide range of tools and services to support data engineering workflows. Databricks also provides certification programs for data professionals who want to demonstrate their expertise in using the platform. One of these certifications is the Databricks Certified Professional Data Engineer exam.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
B. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
E. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Answer: C

Explanation:
Explanation
This is the best configuration for scheduling Structured Streaming jobs for production, as it automatically recovers from query failures and keeps costs low. A new job cluster is created for each run of the job and terminated when the job completes, which saves costs and avoids resource contention. Retries are not needed for Structured Streaming jobs, as they can automatically recover from failures using checkpointing and write-ahead logs. Maximum concurrent runs should be set to 1 to avoid duplicate output or data loss. Verified References: Databricks Certified Data Engineer Professional, under "Monitoring & Logging" section; Databricks Documentation, under "Schedule streaming jobs" section.

NEW QUESTION # 19
Which of the following locations in the Databricks product architecture hosts the notebooks and jobs?

A. JDBC data source
B. Data plane
C. Databricks Filesystem
D. Control plane
E. Databricks web application

Answer: D

Explanation:
Explanation
The answer is Control Pane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Timeline Description automatically generated

NEW QUESTION # 20
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?

A. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A

Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 21
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?

A. TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
B. SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
C. DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
D. SHOW HISTORY table_name AS OF date_sub(current_date(), 1)
E. SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'

Answer: B

Explanation:
Explanation
The answer is SELECT * FROM table_name TIMESTAMP as of date_sub(current_date(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM my_table VERSION AS OF 5238
2.SELECT count(*) FROM my_table@v5238
3.SELECT count(*) FROM delta.`/path/to/my/table@v5238`
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html

NEW QUESTION # 22
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 task A fails during a scheduled run, which statement describes the results of this run?

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

Answer: C

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. Reference:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html

NEW QUESTION # 23
......

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Databricks-Certified-Professional-Data-Engineer Valid Braindumps Sheet | Databricks-Certified-Professional-Data-Engineer Braindump Free

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q18-Q23):

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