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Exam Code: Associate-Developer-Apache-Spark-3.5
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Structured Streaming | 10% | - Streaming Applications
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?
- A. customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1)) - B. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
) - C. customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1)) - D. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
Correct Answer: A 🗳️
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11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?
- A. spark.sql.shuffle.partitions
- B. spark.task.maxFailures
- C. spark.executor.cores
- D. spark.executor.memory
Correct Answer: C 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?
- A. saveAsTable with mode Overwrite
- B. save with mode ErrorIfExists
- C. saveAsTable with mode ErrorIfExists
- D. save with mode Ignore
Correct Answer: C 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?
- A. The number of days specified will be removed from the start date
- B. The number of days specified will be added to the start date
- C. An error message of an invalid parameter will be returned
- D. The same start date will be returned
Correct Answer: B 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:
import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?
- A. def shake_256(raw: str) -> str:
- B. def shake_256(df: pd.Series) -> pd.Series:
- C. def shake_256(df: pd.Series) -> str:
- D. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:
Correct Answer: B 🗳️
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