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Instant Download Databricks : Associate-Developer-Apache-Spark-3.5 Questions & Answers as PDF & Test Engine
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Oct 05, 2026
- No. of Questions: 135 Questions and Answers
- Download Limit: Unlimited
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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
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Structured Streaming | 10% | - Streaming Applications
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
- A. By configuring the option checkpointLocation during writeStream
- B. By configuring the option recoveryLocation during writeStream
- C. By configuring the option checkpointLocation during readStream
- D. By configuring the option recoveryLocation during the SparkSession initialization
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:
def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?
- A. Convert the Pandas UDF from a Series → Series UDF to an Iterator[Series] → Iterator[Series] UDF
- B. Convert the Pandas UDF to a PySpark UDF
- C. Run the in_spanish_inner() function in a mapInPandas() function call
- D. Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?
- A. A Cartesian join
- B. A sort-merge join
- C. A shuffled hash join
- D. A broadcast nested loop join
Correct Answer: C 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
- A. groupBy
- B. select
- C. filter
- D. coalesce
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
In the code block below, aggDF contains aggregations on a streaming DataFrame:
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
- A. replace
- B. aggregate
- C. complete
- D. append
Correct Answer: C 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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