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Excellent Databricks-Certified-Professional-Data-Engineer exam brain dumps offer you high-quality practice questions - ExamBoosts

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
B. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
C. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
D. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
E. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

Answer: E

Explanation:
Explanation
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:
Grant privileges on a database:
https://docs.databricks.com/en/security/auth-authz/table-acls/grant-privileges-database.html Privileges you can grant on Hive metastore objects:
https://docs.databricks.com/en/security/auth-authz/table-acls/privileges.html

NEW QUESTION # 69
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. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
E. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Answer: E

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 toensure 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. References:
* 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 # 70
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.adaptive.coalescePartitions.minPartitionNum
B. spark.sql.autoBroadcastJoinThreshold
C. spark.sql.adaptive.advisoryPartitionSizeInBytes
D. spark.sql.files.openCostInBytes
E. spark.sql.files.maxPartitionBytes

Answer: E

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 References: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.

NEW QUESTION # 71
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 COMMENT line at the beginning of the query
D. They need to add a CREATE DELTA LIVE TABLE table_name AS 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 # 72
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?

A. Triggered, Incremental
B. Triggered, Continuous
C. Once, Continuous
D. Once, Incremental
E. Continuous, Incremental

Answer: B

Explanation:
Explanation
The answer is Triggered, Continuous
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-concepts#-
*Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipeline are updated after their dependent data sources have been updated.
*Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data.

NEW QUESTION # 73
......

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