EXAM DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER INTRODUCTION, EXAM DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER SIMULATOR FREE

Exam Databricks Databricks-Certified-Professional-Data-Engineer Introduction, Exam Databricks-Certified-Professional-Data-Engineer Simulator Free

Exam Databricks Databricks-Certified-Professional-Data-Engineer Introduction, Exam Databricks-Certified-Professional-Data-Engineer Simulator Free

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Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises that test a candidate's ability to design, build, and maintain data pipelines using the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which statement describes Delta Lake Auto Compaction?

  • A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
  • B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
  • C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  • D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
  • E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: D

Explanation:
Explanation
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size


NEW QUESTION # 58
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

  • A. Workspace audit logs
  • B. Executor's log file
  • C. Ganglia
  • D. Cluster Event Log
  • E. Driver's log file

Answer: C


NEW QUESTION # 59
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

  • A. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
  • B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
  • C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
  • D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
  • E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

Answer: D

Explanation:
Explanation
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.


NEW QUESTION # 60
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

  • A. /jobs/runs/get-output
  • B. /jobs/list
  • C. /jobs/runs/get
  • D. /jobs/runs/list
  • E. /jobs/get

Answer: E

Explanation:
Explanation
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.


NEW QUESTION # 61
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 have full permissions on the prod database and can also assign permissions to other users or groups.
  • B. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
  • C. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
  • D. 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.
  • E. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.

Answer: B

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 # 62
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