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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Structured Streaming | 10% | - Streaming Applications
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?
- A. 160
- B. 64
- C. 40
- D. 80
Correct Answer: D 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?
- A. ERROR, WARN, TRACE, OFF
- B. ALL, DEBUG, FAIL, INFO
- C. FATAL, NONE, INFO, DEBUG
- D. WARN, NONE, ERROR, FATAL
Correct Answer: A 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?
- A. Optimizing the layout of Delta files on disk
- B. Collecting persistent table statistics and storing them in the metastore for future use
- C. Dynamically switching join strategies
- D. Improving the performance of single-stage Spark jobs
Correct Answer: C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A job contains multiple stages, and each stage contains multiple tasks.
- B. A job contains multiple tasks, and each task contains multiple stages.
- C. A stage contains multiple jobs, and each job contains multiple tasks.
- D. A stage contains multiple tasks, and each task contains multiple jobs.
Correct Answer: A 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?
- A. Increase the value of spark.sql.shuffle.partitions
- B. Enable dynamic resource allocation to scale resources as needed
- C. Reduce the value of spark.sql.shuffle.partitions
- D. Increase the size of the dataset to create more partitions
Correct Answer: A 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
