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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 2: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 3: Structured Streaming | 10% | - Streaming Applications
|
| Topic 4: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 5: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 6: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 7: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?
- A. employees_df.filter(employees_df.tenure >= 5).show()
- B. filter(employees_df.tenure >= 5)
- C. employees_df.filter(employees_df.tenure >= 5).collect()
- D. employees_df.where(employees_df.tenure >= 5)
Correct Answer: A 🗳️
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.json("path/to/file")
- B. df.write.mode("append").json("path/to/file")
- C. df.write.mode("overwrite").json("path/to/file")
- D. df.write.option("overwrite").json("path/to/file")
Correct Answer: C 🗳️
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?
- A. Stop and restart the SparkSession after each action.
- B. Use a single SparkSession instance for the entire application.
- C. Create a new SparkSession instance before each transformation.
- D. Avoid using a SparkSession and rely on SparkContext only.
Correct Answer: B 🗳️
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)
- A. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
- B. Execute their pyspark shell with the option --remote "https://localhost"
- C. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
- D. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
- E. Execute their pyspark shell with the option --remote "sc://localhost"
Correct Answer: C,E 🗳️
Explanation: Only visible for TestInsides 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: pd.Series) -> pd.Series:
- C. def shake_256(raw: str) -> str:
- D. def shake_256(raw: [str]) -> [str]:
Correct Answer: B 🗳️
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
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