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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Structured Streaming | 10% | - Streaming Applications
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Given a CSV file with the content:
And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?
- A. [Row(name='alladin', age=20)]
- B. [Row(name='bambi'), Row(name='alladin', age=20)]
- C. The code throws an error due to a schema mismatch.
- D. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
Correct Answer: D 🗳️
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A data engineer is working on the DataFrame:
(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?
- A. df.select("Name").distinct().orderBy(df["Name"].desc())
- B. df.select("Name").distinct().orderBy(df["Name"])
- C. df.select("Name").distinct()
- D. df.select("Name").orderBy(df["Name"].asc())
Correct Answer: B 🗳️
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2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.option("overwrite").json("path/to/file")
- B. df.write.mode("append").json("path/to/file")
- C. df.write.mode("overwrite").json("path/to/file")
- D. df.write.json("path/to/file")
Correct Answer: C 🗳️
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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. Improved job execution speed due to reduced logging overhead
- B. Prevention of driver log accumulation during long-running jobs
- C. Loss of access to past job logs and reduced debugging capability for completed jobs
- D. Enhanced executor performance due to reduced log size
Correct Answer: C 🗳️
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A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?
- A. DataFrame.withColumn()
- B. DataFrame.filter()
- C. DataFrame.select()
- D. DataFrame.groupBy().agg()
Correct Answer: D 🗳️
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