[2022] Real Databricks Certified Associate Developer For Apache Spark 3.0 Exam Dumps | DumpsBase

Passing Databricks Certified Associate Developer For Apache Spark 3.0 exam to earn the certification can prove that the candidates have demonstrated an understanding of the basics of the Apache Spark architecture and the ability to apply the Spark DataFrame API to complete individual data manipulation tasks. Databricks Certified Associate Developer For Apache Spark 3.0 dumps of DumpsBase containing real exam questions and answers are written by the top experts to ensure that you can pass the actual exam successfully and smoothly. With DumpsBase Databricks Certified Associate Developer For Apache Spark 3.0 Exam Dumps, you will find the Databricks exam extremely easy to reach the top of your IT career via passing the Databricks Certified Associate Developer For Apache Spark 3.0 test by using the real Databricks Certified Associate Developer For Apache Spark 3.0 dumps questions.

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1. Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?

itemsDf.write.avro(fileLocation)

itemsDf.write.format("avro").mode("ignore").save(fileLocation)

itemsDf.write.format("avro").mode("errorifexists").save(fileLocation)

itemsDf.save.format("avro").mode("ignore").write(fileLocation)

spark.DataFrameWriter(itemsDf).format("avro").write(fileLocation)

2. Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?

1, 10

1, 8

10

7, 9, 10

1, 4, 6, 9

3. 5

4. Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?

transactionsDf.sort(asc(value)).show(10)

transactionsDf.sort(col("value")).show(10)

transactionsDf.sort(col("value").desc()).head()

transactionsDf.sort(col("value").asc()).print(10)

transactionsDf.orderBy("value").asc().show(10)

5. Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)

transactionsDf.cache()

transactionsDf.storage_level('MEMORY_ONLY')

transactionsDf.persist()

transactionsDf.clear_persist()

from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)

6. spark.read.json(filePath, schema=schema)

C. spark.read.json(filePath, schema=schema_of_json(json_schema))

D. spark.read.json(filePath, schema=spark.read.json(json_schema))

7. "left_semi"

8. parquet

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