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

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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0
Last Update: Nov 22, 2024
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The Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 exam is ideal for individuals aspiring to become Apache Spark developers and to demonstrate their proficiency in working with Spark 3.0 on the Databricks platform. It caters to professionals like:

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  • Data analysts
  • Software developers
  • Big data enthusiasts

The Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 exam covers a comprehensive range of topics related to Apache Spark 3.0 and its application on Databricks, including:

  • Apache Spark Architecture Concepts (17% of the exam): Understanding execution modes, adaptive query execution, and more.
  • Apache Spark Architecture Applications (11%): Practical applications of Spark architecture.
  • Apache Spark DataFrame API Applications (72%): Applying DataFrame operations, handling schemas, working with UDFs, and more.
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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Questions and Answers

Questions 1

Which of the following statements about reducing out-of-memory errors is incorrect?

Options:

A.

Concatenating multiple string columns into a single column may guard against out-of-memory errors.

B.

Reducing partition size can help against out-of-memory errors.

C.

Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.

D.

Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.

E.

Decreasing the number of cores available to each executor can help against out-of-memory errors.

Questions 2

The code block shown below should convert up to 5 rows in DataFrame transactionsDf that have the value 25 in column storeId into a Python list. Choose the answer that correctly fills the blanks in

the code block to accomplish this.

Code block:

transactionsDf.__1__(__2__).__3__(__4__)

Options:

A.

1. filter

2. "storeId"==25

3. collect

4. 5

B.

1. filter

2. col("storeId")==25

3. toLocalIterator

4. 5

C.

1. select

2. storeId==25

3. head

4. 5

D.

1. filter

2. col("storeId")==25

3. take

4. 5

E.

1. filter

2. col("storeId")==25

3. collect

4. 5

Questions 3

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.+------+-----------------------------+-------------------+

Options:

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)

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