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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Processing and Performance | - Optimization techniques - Joins and data partitioning - Caching and persistence strategies |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| DataFrame API with PySpark | - DataFrame creation and schema management - Transformations and actions - Built-in functions and expressions |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?
- A. .trigger(availableNow=True)
- B. .trigger(continuous='1 second')
- C. .trigger(continuous=True)
- D. .trigger(processingTime='1 second')
Correct Answer: D 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?
- A. The operation will fail if the Pandas DataFrame exceeds 1000 rows
- B. The operation will load all data into the driver's memory, potentially causing memory overflow
- C. The conversion will automatically distribute the data across worker nodes
- D. Data will be lost during conversion
Correct Answer: B 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:
- A. Spark Streaming, GraphX, and Pandas API on Spark
- B. Spark DataFrames, Structured Streaming, and GraphX
- C. Spark SQL, Pandas API on Spark, and Structured Streaming
- D. Spark DataFrames, Spark SQL, and MLlib
Correct Answer: D 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. psdf.to_pandas()
- B. psdf.to_spark()
- C. psdf.to_dataframe()
- D. psdf.to_pyspark()
Correct Answer: B 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
16 of 55.
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. Transformations are executed immediately to build the lineage graph.
- B. Transformations are evaluated lazily.
- C. Only actions trigger the execution of the transformation pipeline.
- D. The Spark engine optimizes the execution plan during the transformations, causing delays.
- E. The Spark engine requires manual intervention to start executing transformations.
Correct Answer: B,C 🗳️
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