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Databricks Certified Data Engineer Associate Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail. Which feature of Delta Live Tables meets this requirement?

A. Monitoring
B. Expectations
C. Change Data Capture
D. Error Handling

Answer: B

Explanation:
Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.

NEW QUESTION # 94
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could only run the entire program on Sundays.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
D. They could redesign the data model to separate the data used in the final query into a new table.
E. They could submit a feature request with Databricks to add this functionality.

Answer: C

NEW QUESTION # 95
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace spark.read with spark.readStream
B. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
C. Replace format("delta") with format("stream")
D. Replace predict with a stream-friendly prediction function
E. Replace "transactions" with the path to the location of the Delta table

Answer: A

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html

NEW QUESTION # 96
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

A. An automated report needs to be refreshed as quickly as possible.
B. An automated report needs to be runnable by all stakeholders.
C. An automated report needs to be version-controlled across multiple collaborators.
D. An automated report needs to be made reproducible.
E. An automated report needs to be tested to identify errors.

Answer: A

Explanation:
Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. References: Best practices: pools | Databricks on AWS, Best practices: pools - Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google Cloud

NEW QUESTION # 97
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(processingTime= " 5 seconds " )
B. trigger(once= " 5 seconds " )
C. trigger(continuous= " 5 seconds " )
D. trigger( " 5 seconds " )

Answer: A

NEW QUESTION # 98
......

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