Databricks Associate-Developer-Apache-Spark-3.5 Questions & Answers - in .pdf
- Total Q&A: 135
- Update: Aug 16, 2026
- Price: $59.99
- Vendor: Databricks
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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- Total Q&A: 135
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- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 45 |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Exam Price: | $200 USD |
| Passing Score: | 70% |
| Recommended Training: | Databricks Academy Training |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored or onsite proctored |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience with PySpark and DataFrame API |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Topic 2: Using Spark SQL | 20% | - Running SQL queries - Using catalog and metadata APIs - Working with functions and expressions - Integrating Spark SQL with DataFrames |
| Topic 3: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Topic 4: Structured Streaming | 10% | - Output modes and triggers - Fault tolerance and state management - Streaming concepts and architecture - Defining streaming queries |
| Topic 5: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Optimizing transformations and actions - Managing memory and resource usage - Identifying performance bottlenecks |
| Topic 6: Apache Spark Architecture and Components | 20% | - Spark architecture overview - Fault tolerance and garbage collection - Execution and deployment modes - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation |
| Topic 7: Developing Apache Spark DataFrame API Applications | 30% | - Handling missing values and data quality - User-defined functions (UDFs) - Joining and combining datasets - Partitioning and bucketing data - Creating DataFrames and defining schemas - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Reading and writing data in various formats |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:
A) Only actions trigger the execution of the transformation pipeline.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) Transformations are executed immediately to build the lineage graph.
D) The Spark engine requires manual intervention to start executing transformations.
E) Transformations are evaluated lazily.
2. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?
A) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B) Use spark.read.json() with the inferSchema option set to true
C) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
D) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
3. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?
A) An error message of an invalid parameter will be returned
B) The same start date will be returned
C) The number of days specified will be removed from the start date
D) The number of days specified will be added to the start date
4. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
A) pdf.to_pandas()
B) pdf.spark()
C) pdf.to_dataframe()
D) pdf.to_spark()
5. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?
A) df.write.option("overwrite").json("path/to/file")
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.mode("append").json("path/to/file")
D) df.write.json("path/to/file")
Solutions:
| Question # 1 Answer: A,E | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
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