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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
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| 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:
Given this code:
.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:
- A. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
- B. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
- C. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
- D. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
Correct Answer: A 🗳️
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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_spark()
- B. pdf.spark()
- C. pdf.to_pandas()
- D. pdf.to_dataframe()
Correct Answer: A 🗳️
Explanation: Only visible for Real4dumps 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. Increase the memory allocated to the Spark Driver.
- B. Optimize the data processing logic by repartitioning the DataFrame.
- C. Cache large DataFrames to persist them in memory.
- D. Modify the Spark configuration to disable garbage collection
Correct Answer: A 🗳️
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What is the benefit of using Pandas on Spark for data transformations?
Options:
- A. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
- B. It is available only with Python, thereby reducing the learning curve.
- C. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
- D. It computes results immediately using eager execution, making it simple to use.
Correct Answer: A 🗳️
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How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:
- A. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
- B. Configure the application to run in cluster mode instead of local mode.
- C. Increase the number of local threads based on the number of CPU cores.
- D. Set the spark.executor.memory property to a large value.
Correct Answer: C 🗳️
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