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Databricks Associate-Developer-Apache-Spark Exam Test - Associate-Developer-Apache-Spark Latest Examprep

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
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. write
2. mode
3. "overwrite"
4. compression
5. parquet
C. 1. store
2. with
3. "replacement"
4. "compression"
5. path
D. 1. write
2. mode
3. "overwrite"
4. "compression"
5. save
(Correct)
E. 1. save
2. mode
3. "replace"
4. "compression"
5. path

Answer: E

Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir) Solving this question requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.
Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression options are explained in the DataFrameWriter.parquet documentation linked below.
Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.
More info:
- DataFrame.write: pyspark.sql.DataFrame.write - PySpark 3.1.1 documentation
- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 42
Which of the following code blocks efficiently converts DataFrame transactionsDf from 12 into 24 partitions?

A. transactionsDf.repartition(24)
B. transactionsDf.repartition(24, boost=True)
C. transactionsDf.repartition("itemId", 24)
D. transactionsDf.coalesce(24)
E. transactionsDf.repartition()

Answer: A

Explanation:
Explanation
transactionsDf.coalesce(24)
No, the coalesce() method can only reduce, but not increase the number of partitions.
transactionsDf.repartition()
No, repartition() requires a numPartitions argument.
transactionsDf.repartition("itemId", 24)
No, here the cols and numPartitions argument have been mixed up. If the code block would be transactionsDf.repartition(24, "itemId"), this would be a valid solution.
transactionsDf.repartition(24, boost=True)
No, there is no boost argument in the repartition() method.

NEW QUESTION # 43
Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?

A. transactionsDf.drop("predError & value")
B. transactionsDf.drop("predError", "value")
C. transactionsDf.drop(col("predError"), col("value"))
D. transactionsDf.drop(["predError", "value"])
E. transactionsDf.drop(predError, value)

Answer: B

Explanation:
Explanation
More info: pyspark.sql.DataFrame.drop - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 44
Which of the following code blocks applies the boolean-returning Python function evaluateTestSuccess to column storeId of DataFrame transactionsDf as a user-defined function?

A. 1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
2.transactionsDf.withColumn("result", evaluateTestSuccessUDF(col("storeId")))
B. 1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
2.transactionsDf.withColumn("result", evaluateTestSuccessUDF(storeId))
C. 1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
3.transactionsDf.withColumn("result", evaluateTestSuccessUDF(col("storeId")))
D. 1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.IntegerType())
3.transactionsDf.withColumn("result", evaluateTestSuccess(col("storeId")))
E. 1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
3.transactionsDf.withColumn("result", evaluateTestSuccess(col("storeId")))

Answer: C

Explanation:
Explanation
Recognizing that the UDF specification requires a return type (unless it is a string, which is the default) is important for solving this question. In addition, you should make sure that the generated UDF (evaluateTestSuccessUDF) and not the Python function (evaluateTestSuccess) is applied to column storeId.
More info: pyspark.sql.functions.udf - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 45
Which is the highest level in Spark's execution hierarchy?

A. Executor
B. Job
C. Stage
D. Task
E. Slot

Answer: B

NEW QUESTION # 46
......

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