DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARKDatabricks Certified Associate Developer for Apache Spark 3.0
180 Questions and Answers Experienced specialists selected 180 questions for this exam. All answers are verified to ensure correctness.
Last Updated May 14, 2024 Ace your exams with our consistently updated DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK exam dumps.
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DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Online Practice Questions and Answers
The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)
A. 1. save
2.
mode
3.
"ignore"
4.
"compression"
5.
path
B. 1. store
2.
with
3.
"replacement"
4.
"compression"
5.
path
C. 1. write
2.
mode
3.
"overwrite"
4.
"compression"
5.
save
(Correct)
D. 1. save
2.
mode
3.
"replace"
4.
"compression"
5.
path
E. 1. write
2.
mode
3.
"overwrite"
4.
compression
5.
parquet
Which of the following statements about executors is correct?
A. Executors are launched by the driver.
B. Executors stop upon application completion by default.
C. Each node hosts a single executor.
D. Executors store data in memory only.
E. An executor can serve multiple applications.
Which of the following code blocks returns about 150 randomly selected rows from the 1000-row DataFrame transactionsDf, assuming that any row can appear more than once in the returned DataFrame?
A. transactionsDf.resample(0.15, False, 3142)
B. transactionsDf.sample(0.15, False, 3142)
C. transactionsDf.sample(0.15)
D. transactionsDf.sample(0.85, 8429)
E. transactionsDf.sample(True, 0.15, 8261)
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Databricks DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK exam official information: The Databricks Certified Associate Developer for Apache Spark certification exam assesses the understanding of the Spark DataFrame API and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session.
