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Free PDF Databricks-Certified-Professional-Data-Engineer - Reliable Reliable Databricks Certified Professional Data Engineer Exam Test Testking

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.

Which kind of the test does the above line exemplify?

A. Unit
B. Integration
C. functional
D. Manual

Answer: A

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function.
Testing a mathematical function that calculates the area under acurve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
References:
* Software Testing Fundamentals: Unit Testing

NEW QUESTION # 125
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A. Regex
B. C++
C. Julia
D. Scala Datasets
E. pyspsark.ml.feature

Answer: A

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
* https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
* https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html

NEW QUESTION # 126
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:
A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Answer: B

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints: https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 127
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?

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

Answer: C

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 # 128
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?

A. They can institute a retry policy for the task that periodically fails
B. They can utilize a Jobs cluster for each of the tasks in the Job
C. They can institute a retry policy for the entire Job
D. They can set up the Job to run multiple times ensuring that at least one will complete
E. They can observe the task as it runs to try and determine why it is failing

Answer: A

NEW QUESTION # 129
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