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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| DataFrame API with PySpark | - Transformations and actions - Built-in functions and expressions - DataFrame creation and schema management |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Structured Streaming Basics | - Windowed aggregations in streaming - Streaming DataFrames |
| Data Processing and Performance | - Joins and data partitioning - Optimization techniques - Caching and persistence strategies |
| Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?
A) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
B) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
C) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
D) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
2. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?
A) df1.orderBy(col("count").desc(), col("Name").asc())
B) df1.orderBy("Name", "count")
C) df1.sort("Name", "count")
D) df1.orderBy(col("Name").desc(), col("count").asc())
3. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?
A) Enhanced executor performance due to reduced log size
B) Loss of access to past job logs and reduced debugging capability for completed jobs
C) Improved job execution speed due to reduced logging overhead
D) Prevention of driver log accumulation during long-running jobs
4. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:
A) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
5. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?
A) CPU Cores
B) Driver Nodes
C) Worker Nodes
D) Executors
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: D |
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