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 Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Dumps with Practice Exam Questions Answers

Questions: 180 questions

Last Update: Aug 14, 2022

Databricks Certification Exam Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions and Answers

Question # 1

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

A.

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

B.

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

C.

Use a narrow transformation to reduce the number of partitions.

D.

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.

Question # 2

Which of the following is a problem with using accumulators?

A.

Only unnamed accumulators can be inspected in the Spark UI.

B.

Only numeric values can be used in accumulators.

C.

Accumulator values can only be read by the driver, but not by executors.

D.

Accumulators do not obey lazy evaluation.

E.

Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.

Question # 3

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

A.

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

B.

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

C.

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

D.

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

E.

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

Question # 4

Which of the following code blocks reads the parquet file stored at filePath into DataFrame itemsDf, using a valid schema for the sample of itemsDf shown below?

Sample of itemsDf:

1.+------+-----------------------------+-------------------+

2.|itemId|attributes |supplier |

3.+------+-----------------------------+-------------------+

4.|1 |[blue, winter, cozy] |Sports Company Inc.|

5.|2 |[red, summer, fresh, cooling]|YetiX |

6.|3 |[green, summer, travel] |Sports Company Inc.|

7.+------+-----------------------------+-------------------+

A.

1.itemsDfSchema = StructType([

2. StructField("itemId", IntegerType()),

3. StructField("attributes", StringType()),

4. StructField("supplier", StringType())])

5.

6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath)

B.

1.itemsDfSchema = StructType([

2. StructField("itemId", IntegerType),

3. StructField("attributes", ArrayType(StringType)),

4. StructField("supplier", StringType)])

5.

6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath)

C.

1.itemsDf = spark.read.schema('itemId integer, attributes , supplier string').parquet(filePath)

D.

1.itemsDfSchema = StructType([

2. StructField("itemId", IntegerType()),

3. StructField("attributes", ArrayType(StringType())),

4. StructField("supplier", StringType())])

5.

6.itemsDf = spark.read.schema(itemsDfSchema).parquet(filePath)

E.

1.itemsDfSchema = StructType([

2. StructField("itemId", IntegerType()),

3. StructField("attributes", ArrayType([StringType()])),

4. StructField("supplier", StringType())])

5.

6.itemsDf = spark.read(schema=itemsDfSchema).parquet(filePath)

Question # 5

The code block shown below should return a copy of DataFrame transactionsDf without columns value and productId and with an additional column associateId that has the value 5. Choose the

answer that correctly fills the blanks in the code block to accomplish this.

transactionsDf.__1__(__2__, __3__).__4__(__5__, 'value')

A.

1. withColumn

2. 'associateId'

3. 5

4. remove

5. 'productId'

B.

1. withNewColumn

2. associateId

3. lit(5)

4. drop

5. productId

C.

1. withColumn

2. 'associateId'

3. lit(5)

4. drop

5. 'productId'

D.

1. withColumnRenamed

2. 'associateId'

3. 5

4. drop

5. 'productId'

E.

1. withColumn

2. col(associateId)

3. lit(5)

4. drop

5. col(productId)

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