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The Databricks Databricks-Certified-Data-Engineer-Associate Exam consists of 60 multiple-choice questions and is designed to test your understanding of Databricks concepts, including data engineering, data processing, data storage, and data analysis. To pass the exam, you must score at least 70%. Databricks-Certified-Data-Engineer-Associate exam can be taken online and is available in several languages, including English, Spanish, and Chinese. Upon passing the exam, you will receive a Databricks Certified Data Engineer Associate certification, which is recognized globally as a mark of expertise in big data processing using Databricks.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam designed for professionals seeking to validate their proficiency in data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate exam covers various aspects of Databricks such as data transformation, ETL processes, data modeling, and data warehousing. Databricks Certified Data Engineer Associate Exam certification is globally recognized and demonstrates the candidate's expertise in Databricks data engineering.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
A data engineer is running a Spark SQL job on Databricks and notices that joins between a small lookup table and a large fact table are slow. The engineer wants Spark to automatically send the small lookup table to all executors to accelerate the join.
Which configuration parameter should the data engineer adjust?

A. spark.sql.shuffle.partitions
B. spark.executor.memory
C. spark.default.parallelism
D. spark.sql.autoBroadcastJoinThreshold

Answer: D

NEW QUESTION # 186
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)

A. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'
B. SELECT * FROM students VERSION AS OF 5
C. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'
D. SELECT * FROM students@v4
E. SELECT * FROM students FROM HISTORY VERSION AS OF 3

Answer: A,D

NEW QUESTION # 187

Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

A. region_sales= sales_df.groupBy("category").sum(nsales_amount").alias("t_otal_sales_amounl")
B. region_sales = sales_df.groupBy("region").agg(sum("sales_amountM).alias("total_sales_amount"))
C. region sales - sales_df.agg(sum("sales_amount").groupBy("region").alias("total sales amount"))
D. region_sales = sales_df. sum ("salen_aiTiount") . groupBy ("region") .alias ("total_sale3_amount")

Answer: B

NEW QUESTION # 188
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages
B. The ability to support batch and streaming workloads
C. The ability to distribute complex data operations
D. The ability to set up alerts for query failures
E. The ability to collaborate in real time on a single notebook

Answer: B

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more

NEW QUESTION # 189
A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?

Answer: A

NEW QUESTION # 190
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