New Databricks-Certified-Professional-Data-Engineer Dumps Questions & Databricks-Certified-Professional-Data-Engineer Practice Tests

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Databricks Certified Professional Data Engineer exam is a comprehensive assessment of a candidate's ability to design, implement, and manage data pipelines on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the candidate's knowledge of best practices for building efficient and scalable data pipelines that can handle large volumes of data.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q121-Q126):

NEW QUESTION # 121
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

A. TF-IDF
B. Logistic regression
C. Linear regression
D. Decision trees

Answer: C

NEW QUESTION # 122
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day.
At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
B. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
C. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
E. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

Answer: B

Explanation:
Explanation
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time1. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job2.
The other options are not correct, because:
Option A is incorrect because triggering batches more frequently does not allow idle executors to begin processing the next batch while longer running tasks from previous batches finish. In fact, the opposite is true. Triggering batches more frequently may cause concurrent batches to compete for the same resources and cause contention and backpressure2. This can degrade the performance and stability of the streaming job.
Option B is incorrect because increasing the trigger interval to 30 seconds is not a good practice to ensure no records are dropped. Increasing the trigger interval means that the streaming query will process larger batches of data less frequently, which can increase the risk of spill, memory pressure, and timeouts12. This can also increase the latency and reduce the throughput of the streaming job.
Option C is incorrect because the trigger interval can be modified without modifying the checkpoint directory. The checkpoint directory stores the metadata and state of the streaming query, such as the offsets, schema, and configuration3. Changing the trigger interval does not affect the state of the streaming query, and does not require a new checkpoint directory. However, changing the number of shuffle partitions may affect the state of the streaming query, and may require a new checkpoint directory4.
Option D is incorrect because using the trigger once option and configuring a Databricks job to execute the query every 10 seconds does not ensure that all backlogged records are processed with each batch. The trigger once option means that the streaming query will process all the available data in the source and then stop5. However, this does not guarantee that the query will finish processing within 10 seconds, especially if there area lot of records in the source. Moreover, configuring a Databricks job to execute the query every 10 seconds may cause overlapping or missed batches, depending on the execution time of the query.
References: Memory Management Overview, Structured Streaming Performance Tuning Guide, Checkpointing, Recovery Semantics after Changes in a Streaming Query, Triggers

NEW QUESTION # 123
Which of the following SQL statements can replace python variables in Databricks SQL code, when the notebook is set in SQL mode?
1.%python
2.table_name = "sales"
3.schema_name = "bronze"
4.
5.%sql
6.SELECT * FROM ____________________

A. SELECT * FROM {schem_name.table_name}
B. SELECT * FROM schema_name.table_name
C. SELECT * FROM ${schema_name}.${table_name}
D. SELECT * FROM f{schema_name.table_name}

Answer: C

Explanation:
Explanation
The answer is, SELECT * FROM ${schema_name}.${table_name}
%python
table_name = "sales"
schema_name = "bronze"
%sql
SELECT * FROM ${schema_name}.${table_name}
${python variable} -> Python variables in Databricks SQL code

NEW QUESTION # 124
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

Answer: A

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
References:
* Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

NEW QUESTION # 125
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. "Can Manage" privileges on the required cluster
C. "Can Restart" privileges on the required cluster
D. Cluster creation allowed. "Can Attach To" privileges on the required cluster
E. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

NEW QUESTION # 126
......

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