Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Structured Streaming | 10% | - Streaming Applications
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Pandas API on Spark | 5% | - Pandas API
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
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 DataFrames, Structured Streaming, and GraphX
- B. Spark Streaming, GraphX, and Pandas API on Spark
- C. Spark DataFrames, Spark SQL, and MLlib
- D. Spark SQL, Pandas API on Spark, and Structured Streaming
Correct Answer: C 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?
- A. regions_dict = dict(regions.take(3))
- B. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
- C. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
- D. regions_dict = regions.select("region_id", "region_name").take(3)
Correct Answer: B 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
Given the code:
df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?
- A. When the groupBy transformation is applied
- B. When the filter transformation is applied
- C. When the show action is applied
- D. When the count action is applied
Correct Answer: D 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
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. 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.
- B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
- C. 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.
- D. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
Correct Answer: B 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
36 of 55.
What is the main advantage of partitioning the data when persisting tables?
- A. It optimizes by reading only the relevant subset of data from fewer partitions.
- B. It ensures that data is loaded into memory all at once for faster query execution.
- C. It automatically cleans up unused partitions to optimize storage.
- D. It compresses the data to save disk space.
Correct Answer: A 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
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