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Databricks is an American-based technology company that develops software that manages big data via Apache Spark. Their software helps different organizations to process and analyze large data sets efficiently. Therefore, there is a need to certify professionals to handle these technologies. The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is designed to validate the knowledge and proficiency of data engineers in designing and building effective and scalable data engineering solutions on the Databricks platform.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
You are working on a process to query the table based on batch date, and batch date is an input parameter and expected to change every time the program runs, what is the best way to we can parameterize the query to run without manually changing the batch date?

A. Create a notebook parameter for batch date and assign the value to a python variable and use a spark data frame to filter the data based on the python variable
B. Create a dynamic view that can calculate the batch date automatically and use the view to query the data
C. Manually edit code every time to change the batch date
D. Store the batch date in the spark configuration and use a spark data frame to filter the data based on the spark configuration.
E. There is no way we can combine python variable and spark code

Answer: A

Explanation:
Explanation
The answer is, Create a notebook parameter for batch date and assign the value to a python variable and use a spark data frame to filter the data based on the python variable

NEW QUESTION # 106
A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

A. They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query
B. They need to add a live. prefix prior to json. in the FROM line
C. They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query
D. They need to add a COMMENT line at the beginning of the query
E. They need to add the cloud_files(...) wrapper to the JSON file path

Answer: A

NEW QUESTION # 107
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 merged.
B. They are deleted.
C. They are updated.
D. They are ignored.
E. They are inserted.

Answer: D

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 # 108
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

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

Answer: D

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 # 109
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track updates to the static Delta table.
B. The checkpoint directory will be used to track state information for the unique keys present in the join.
C. Stream-static joins cannot use static Delta tables because of consistency issues.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Answer: E

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 110
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