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Databricks Certified Professional Data Engineer Exam Sample Questions (Q40-Q45):
NEW QUESTION # 40
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?
- A. spark.sql.autoBroadcastJoinThreshold
- B. spark.sql.files.openCostInBytes
- C. spark.sql.files.maxPartitionBytes
- D. spark.sql.adaptive.coalescePartitions.minPartitionNum
- E. spark.sql.adaptive.advisoryPartitionSizeInBytes
Answer: C
Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.
NEW QUESTION # 41
You are currently looking at a table that contains data from an e-commerce platform, each row contains a list of items(Item number) that were present in the cart, when the customer makes a change to the cart the entire information is saved as a separate list and appended to an existing list for the duration of the customer session, to identify all the items customer bought you have to make a unique list of items, you were asked to create a unique item's list that was added to the cart by the user, fill in the blanks of below query by choosing the appropriate higher-order function?
Note: See below sample data and expected output.
Schema: cartId INT, items Array<INT>
Fill in the blanks:
Fill in the blanks:
SELECT cartId, _(_(items)) FROM carts
- A. ARRAY_DISTINCT, ARRAY_UNION
- B. FLATTEN, ARRAY_DISTINCT
- C. ARRAY_DISTINCT, FLATTEN
- D. ARRAY_UNION, ARRAY_DISCINT
- E. ARRAY_DISTINCT, ARRAY_FLATTEN
Answer: C
Explanation:
Explanation
FLATTEN -> Transforms an array of arrays into a single array.
ARRAY_DISTINCT -> The function returns an array of the same type as the input argument where all duplicate values have been removed.
Table Description automatically generated
NEW QUESTION # 42
A new user who currently does not have access to the catalog or schema is requesting access to the customer table in sales schema, but the customer table contains sensitive information, so you have decided to create view on the table excluding columns that are sensitive and granted access to the view GRANT SELECT ON view_name to user@company.com but when the user tries to query the view, gets the error view does not exist. What is the issue preventing user to access the view and how to fix it?
- A. User has to be the owner of the view
- B. User requires USAGE privilege on Sales schema
- C. User requires SELECT on the underlying table
- D. User requires to be put in a special group that has access to PII data
- E. User needs ADMIN privilege on the view
Answer: B
Explanation:
Explanation
The answer is User requires USAGE privilege on Sales schema,
Data object privileges - Azure Databricks | Microsoft Docs
GRANT USAGE ON SCHEMA sales TO user@company.com;
*USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.
NEW QUESTION # 43
Which is a key benefit of an end-to-end test?
- A. It closely simulates real world usage of your application.
- B. It pinpoint errors in the building blocks of your application.
- C. It makes it easier to automate your test suite
- D. It provides testing coverage for all code paths and branches.
Answer: A
Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
Reference:
Software Testing: End-to-End Testing
NEW QUESTION # 44
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 the referenced existing all purpose cluster.
- B. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
- C. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- 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: C
Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled. Verified Reference: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.
NEW QUESTION # 45
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