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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 2: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 3: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 4: Structured Streaming | 10% | - Streaming Applications
|
| Topic 5: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 7: Using Pandas API on Spark | 5% | - Pandas API
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
- A. APPEND
- B. AGGREGATE
- C. COMPLETE
- D. REPLACE
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?
- A. Fetch the logs by running a Spark job with the spark-sql CLI tool.
- B. Locate the executor logs on the Spark master node, typically under the /tmp directory.
- C. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
- D. Use the command spark-submit with the -verbose flag to print the logs to the console.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
What is the benefit of using Pandas on Spark for data transformations?
Options:
- A. It is available only with Python, thereby reducing the learning curve.
- B. It computes results immediately using eager execution, making it simple to use.
- C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
- D. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
- A. Enable dynamic resource allocation to scale resources as needed
- B. Increase the value of spark.sql.shuffle.partitions
- C. Reduce the value of spark.sql.shuffle.partitions
- D. Increase the size of the dataset to create more partitions
Correct Answer: B 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?
- A. Optimize the data processing logic by repartitioning the DataFrame.
- B. Modify the Spark configuration to disable garbage collection
- C. Increase the memory allocated to the Spark Driver.
- D. Cache large DataFrames to persist them in memory.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
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