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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Structured Streaming | 10% | - Streaming Applications
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:
- A. A job contains multiple tasks, and each task contains multiple stages.
- B. A job contains multiple stages, and each stage contains multiple tasks.
- C. A stage contains multiple jobs, and each job contains multiple tasks.
- D. A stage contains multiple tasks, and each task contains multiple jobs.
Correct Answer: B 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?
- A. filtered_users = raw_users.na.drop("any")
- B. filtered_users = raw_users.dropna(how="all")
- C. filtered_users = raw_users.dropna(how="any")
- D. filtered_users = raw_users.na.drop("all")
Correct Answer: C 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.mode("overwrite").parquet("/data/output")
- B. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- C. df.write.mode("append").partitionBy("country").parquet("/data/output")
- D. df.write.partitionBy("country").parquet("/data/output")
Correct Answer: B 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A data analyst wants to add a column date derived from a timestamp column.
Options:
- A. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
- B. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
- C. dates_df.withColumn("date", f.to_date("timestamp")).show()
- D. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
Correct Answer: C 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?
- A. def shake_256(raw: pd.Series) -> pd.Series:
- B. def shake_256(raw: [str]) -> [str]:
- C. def shake_256(raw: [pd.Series]) -> pd.Series:
- D. def shake_256(raw: str) -> str:
Correct Answer: A 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
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