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The GAQM Databricks-Certified-Data-Engineer-Associate Exam is a certification that validates a candidate's proficiency in using Databricks to build and maintain data pipelines. Databricks is a cloud-based data platform that allows users to easily process large amounts of data using Apache Spark. Databricks Certified Data Engineer Associate Exam certification is designed for data engineers who want to demonstrate their expertise in building scalable, reliable, and performant data pipelines.

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Databricks Certified Data Engineer Associate Exam covers a range of topics related to data engineering, including data ingestion, ETL (extract, transform, load) processes, data modeling, and data warehousing. Databricks-Certified-Data-Engineer-Associate Exam also covers Databricks-specific tools and technologies, such as Databricks Delta Lake and Databricks Runtime. Passing the exam requires a deep understanding of these topics and the ability to apply that knowledge in real-world scenarios. Successful candidates will have demonstrated their ability to design and implement reliable, scalable, and efficient data pipelines using Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: D

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 113
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:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="once")
B. trigger(parallelBatch=True)
C. trigger(continuous="once")
D. trigger(availableNow=True)
E. processingTime(1)

Answer: D

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter

NEW QUESTION # 114
A Structured Streaming job is stopped and restarted after a code change. The engineer wants the job to resume from where it left off rather than reprocessing all source data.
Which configuration is required?

A. Setting outputMode to complete
B. Specifying a stable checkpointLocation on the write stream
C. Setting maxFilesPerTrigger to 1
D. Enabling mergeSchema on the target table

Answer: B

Explanation:
The checkpoint location stores stream progress and offset information, allowing the query to resume exactly where it stopped after a restart or failure. Each streaming query must have its own dedicated checkpoint directory; reusing or deleting it causes reprocessing or incorrect results.

NEW QUESTION # 115
A data engineer has inherited a Databricks pipeline from a previous team. The pipeline is missing SLAs and costs more than the allotted budget. On analysis, it is noted that the cluster is not being fully utilized, and the dataset is getting skewed. How should the data engineer resolve this issue?

A. Repartition the dataset to have it be more optimally spread across all nodes.
B. Increase the executor memory for the job.
C. Increase the number of executors for the job.
D. Use coalesce() on the dataset to merge partitions and reduce skew.

Answer: A

Explanation:
Repartitioning the dataset ensures data is evenly distributed across all nodes, reducing skew, improving cluster utilization, and helping control costs while meeting SLAs.

NEW QUESTION # 116
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with time-related data in specified intervals
B. An ability to work with an array of tables for procedural automation
C. An ability to work with complex, nested data ingested from JSON files
D. An ability to work with data within certain partitions and windows
E. An ability to work with data in a variety of types at once

Answer: C

Explanation:
Explanation
Array functions in Spark SQL are primarily used for working with arrays and complex, nested data structures, such as those often encountered when ingesting JSON files. These functions allow you to manipulate and query nested arrays and structures within your data, making it easier to extract and work with specific elements or values within complex data formats. While some of the other options (such as option A for working with different data types) are features of Spark SQL or SQL in general, array functions specifically excel at handling complex, nested data structures like those found in JSON files.

NEW QUESTION # 117
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