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Databricks Associate-Developer-Apache-Spark-3.5 valid study dumps : Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Sep 26, 2026
- Q&As: 135 Questions and Answers
About Databricks Certified Associate Developer for Apache Spark 3.5 - Python - Associate-Developer-Apache-Spark-3.5 Valid Dumps
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Identifying performance bottlenecks - Debugging and logging - Managing memory and resource usage |
| Structured Streaming | 10% | - Fault tolerance and state management - Output modes and triggers - Defining streaming queries - Streaming concepts and architecture |
| Apache Spark Architecture and Components | 20% | - Spark architecture overview - Execution and deployment modes - Fault tolerance and garbage collection - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation |
| Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Selecting, renaming, and modifying columns - Partitioning and bucketing data - User-defined functions (UDFs) - Handling missing values and data quality - Creating DataFrames and defining schemas - Filtering, sorting, and aggregating data - Reading and writing data in various formats |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Working with functions and expressions - Running SQL queries - Integrating Spark SQL with DataFrames |
| Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Spark Connect architecture - Running applications via Spark Connect |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question #1
26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?
- A. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
- B. df_user_non_pii = df_user.drop("name", "email", "birthdate")
- C. df_user_non_pii = df_user.select("name", "email", "birthdate")
- D. df_user_non_pii = df_user.remove("name", "email", "birthdate")
Correct Answer: B 🗳️
Explanation: Only visible for ValidDumps members. You can sign-up / login (it's free).
Question #2
A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)
B)
C)
D)
- A. exploded_df = exploded_df.select("record_datetime", "record_exploded")
- B. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - C. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - D. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
Correct Answer: C 🗳️
Explanation: Only visible for ValidDumps members. You can sign-up / login (it's free).
Question #3
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. select
- B. coalesce
- C. filter
- D. groupBy
Correct Answer: D 🗳️
Explanation: Only visible for ValidDumps members. You can sign-up / login (it's free).
Question #4
An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?
- A. Debuggability: the ability to perform interactive debugging directly from the application code
- B. Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
- C. Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
- D. Stability: isolation of application code and dependencies from each other and the Spark driver
Correct Answer: B 🗳️
Explanation: Only visible for ValidDumps members. You can sign-up / login (it's free).
Question #5
You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?
- A. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
- B. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
- C. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
- D. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
Correct Answer: D 🗳️
Explanation: Only visible for ValidDumps members. You can sign-up / login (it's free).
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