Pass Databricks Certified Associate Developer for Apache Spark 3.0 Exam With Real Questions [2022] OF ITExamShop

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You can pass Databricks Certified Associate Developer for Apache Spark 3.0 exam with real Databricks Certified Associate Developer for Apache Spark 3.0 questions of ITExamShop online. We know, earning Databricks Certified Associate Developer for Apache Spark 3.0 certification has 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. The Databricks Certified Associate Developer for Apache Spark 3.0 real exam questions of ITExamShop are written by the top IT professionals, who created the Databricks Certified Associate Developer for Apache Spark 3.0 exam questions based on the exam objectives and topics. We ensure that you can pass Databricks Certified Associate Developer for Apache Spark 3.0 exam in the first try with the real questions of ITExamShop.

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1. 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)

2. Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?

Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.

Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.

Use a narrow transformation to reduce the number of partitions.

Use a wide transformation to reduce the number of partitions. Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.

3. The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to

transactionNumber. Find the error.

Code block:

transactionsDf.withColumn("transactionNumber", "transactionId")

The arguments to the withColumn method need to be reordered.

The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.

The copy() operator should be appended to the code block to ensure a copy is returned.

Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.

The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.

4. Which of the following describes a valid concern about partitioning?

A shuffle operation returns 200 partitions if not explicitly set.

Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.

No data is exchanged between executors when coalesce() is run.

Short partition processing times are indicative of low skew.

The coalesce() method should be used to increase the number of partitions.

5. parquet

6. 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)

7. Which of the following is a viable way to improve Spark's performance when dealing with large amounts of data, given that there is only a single application running on the cluster?

Increase values for the properties spark.default.parallelism and spark.sql.shuffle.partitions

Decrease values for the properties spark.default.parallelism and spark.sql.partitions

Increase values for the properties spark.sql.parallelism and spark.sql.partitions

Increase values for the properties spark.sql.parallelism and spark.sql.shuffle.partitions

Increase values for the properties spark.dynamicAllocation.maxExecutors, spark.default.parallelism, and spark.sql.shuffle.partitions

8. spark.read.options("modifiedBefore", "2029-03-

20T05:44:46").schema(schema).load(filePath)

The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark's DataFrameReader is incorrect.

Columns in the schema definition use the wrong object type and the syntax of the call to Spark's DataFrameReader is incorrect.

The data type of the schema is incompatible with the schema() operator and the modification date threshold is specified incorrectly.

Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.

Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.

9. Which of the following describes a difference between Spark's cluster and client execution modes?

In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.

In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.

In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.

In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.

In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

10. spark.sql(statement).drop("value", "storeId", "attributes")

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11. Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?

transactionsDf.filter(productId==3 or productId<1)

transactionsDf.filter((col("productId")==3) or (col("productId")<1))

transactionsDf.filter(col("productId")==3 | col("productId")<1)

transactionsDf.where("productId"=3).or("productId"<1))

transactionsDf.filter((col("productId")==3) | (col("productId")<1))

12. Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

Slot is another name for executor.

There must be less executors than tasks.

An executor runs on a single core.

There must be more slots than tasks.

Tasks run in parallel via slots.

13. Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format

month/day/year in column transactionDateFormatted?

Excerpt of DataFrame transactionsDf:

transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFor matted")

transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

14. count()

15. Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?

spark.read.json(filePath)

spark.read.path(filePath, source="json")

spark.read().path(filePath)

spark.read().json(filePath)

spark.read.path(filePath)

16. 5

17. col("value")

18. The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.

Code block:

transactionsDf.filter(col('predError').in([3, 6])).count()

The number of rows cannot be determined with the count() operator.

Instead of filter, the select method should be used.

The method used on column predError is incorrect.

Instead of a list, the values need to be passed as single arguments to the in operator.

Numbers 3 and 6 need to be passed as string variables.

19. dfDates = dfDates.withColumnRenamed("date", to_datetime("date", "yyyy-MM-dd HH:mm:ss"))

E. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])

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Pass Databricks Certified Associate Developer for Apache Spark 3.0 Exam With Real Questions [2022] OF ITExamShop

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