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


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

Question # 1

Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?

A.

transactionsDf.count("productId").distinct()

B.

transactionsDf.groupBy("productId").agg(col("value").count())

C.

transactionsDf.count("productId")

D.

transactionsDf.groupBy("productId").count()

E.

transactionsDf.groupBy("productId").select(count("value"))

Question # 2

The code block displayed below contains at least one error. The code block should return a DataFrame with only one column, result. That column should include all values in column value from

DataFrame transactionsDf raised to the power of 5, and a null value for rows in which there is no value in column value. Find the error(s).

Code block:

1.from pyspark.sql.functions import udf

2.from pyspark.sql import types as T

3.

4.transactionsDf.createOrReplaceTempView('transactions')

5.

6.def pow_5(x):

7. return x**5

8.

9.spark.udf.register(pow_5, 'power_5_udf', T.LongType())

10.spark.sql('SELECT power_5_udf(value) FROM transactions')

A.

The pow_5 method is unable to handle empty values in column value and the name of the column in the returned DataFrame is not result.

B.

The returned DataFrame includes multiple columns instead of just one column.

C.

The pow_5 method is unable to handle empty values in column value, the name of the column in the returned DataFrame is not result, and the SparkSession cannot access the transactionsDf

DataFrame.

D.

The pow_5 method is unable to handle empty values in column value, the name of the column in the returned DataFrame is not result, and Spark driver does not call the UDF function

appropriately.

E.

The pow_5 method is unable to handle empty values in column value, the UDF function is not registered properly with the Spark driver, and the name of the column in the returned DataFrame is

not result.

Question # 3

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()

A.

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

B.

Instead of filter, the select method should be used.

C.

The method used on column predError is incorrect.

D.

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

E.

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

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps FAQs

1. What is the Databricks Certified Associate Developer for Apache Spark 3.0 Exam?

The Databricks Certified Associate Developer for Apache Spark 3.0 exam assesses candidates’ understanding of the Spark DataFrame API. It evaluates their ability to apply the API for basic data manipulation tasks within a Spark session.

2. What topics are covered in the Databricks Certified Associate Developer for Apache Spark 3.0 Exam?

The Databricks Certified Associate Developer for Apache Spark 3.0 exam focuses on three key areas:

  • Apache Spark Architecture Concepts (17% of the exam)
  • Apache Spark Architecture Applications (11% of the exam)
  • Apache Spark DataFrame API Applications (72% of the exam)

3. How long is the Databricks Certified Associate Developer for Apache Spark 3.0 exam duration?

Testers have 120 minutes to complete the Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 certification exam.

4. How many questions are there in the Databricks Certified Associate Developer for Apache Spark 3.0 Exam?

The Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 exam consists of 60 multiple-choice questions distributed across the specified topics.

5. Is Databricks Certified Associate Developer for Apache Spark 3.0 worth it?

Earning the Databricks Certified Associate Developer for Apache Spark 3.0 certification validates your expertise and sets you apart from other candidates. It demonstrates your ability to leverage Spark for Big Data processing on Databricks, making you a valuable asset in the job market.

6. What is the difference between the Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 and Databricks-Certified-Data-Engineer-Associate Exams?

Here's a breakdown of the key differences between the Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 and Databricks-Certified-Data-Engineer-Associate exams:

  • Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0: The Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam focuses developing Spark applications using Databricks. This exam is ideal for data analysts, developers, and engineers building Spark applications.
  • Databricks-Certified-Data-Engineer-Associate: The Databricks-Certified-Data-Engineer-Associate Exam focuses on broader scope encompassing data engineering concepts on Databricks. This exam is ideal for data engineers who design, develop, and maintain data pipelines on Databricks.

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