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Databricks Updated Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Questions and Answers by nia

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Overview :

Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Exam Code: Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Dumps
Vendor: Databricks Certification: Databricks Certification
Questions: 180 Q&A's Shared By: nia
Question 24

The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to

numeric and nullable column predError in DataFrame transactionsDf. Find the error.

Code block:

1.def add_2_if_geq_3(x):

2. if x is None:

3. return x

4. elif x >= 3:

5. return x+2

6. return x

7.

8.add_2_if_geq_3_udf = udf(add_2_if_geq_3)

9.

10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))

Options:

A.

The operator used to adding the column does not add column predErrorAdded to the DataFrame.

B.

Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.

C.

The udf() method does not declare a return type.

D.

UDFs are only available through the SQL API, but not in the Python API as shown in the code block.

E.

The Python function is unable to handle null values, resulting in the code block crashing on execution.

Discussion
Question 25

Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?

Sample of DataFrame transactionsDf:

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

2.|transactionId|predError|value|storeId|productId| f|

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

4.| 1| 3| 4| 25| 1|null|

5.| 2| 6| 7| 2| 2|null|

6.| 3| 3| null| 25| 3|null|

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

Options:

A.

transactionsDf.drop(col("value"), col("predError"))

B.

transactionsDf.drop("predError", "value")

C.

transactionsDf.drop(value, predError)

D.

transactionsDf.drop(["predError", "value"])

E.

transactionsDf.drop([col("predError"), col("value")])

Discussion
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Question 26

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)

Discussion
Question 27

Which of the following statements about RDDs is incorrect?

Options:

A.

An RDD consists of a single partition.

B.

The high-level DataFrame API is built on top of the low-level RDD API.

C.

RDDs are immutable.

D.

RDD stands for Resilient Distributed Dataset.

E.

RDDs are great for precisely instructing Spark on how to do a query.

Discussion
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