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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Topic 2: Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Topic 3: Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Topic 4: Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Topic 5: Data Processing and Performance | - Caching and persistence strategies - Joins and data partitioning - Optimization techniques |
| Topic 6: DataFrame API with PySpark | - Built-in functions and expressions - DataFrame creation and schema management - Transformations and actions |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?
- A. Same number as the cluster executors
- B. 5
- C. 20
- D. 10
Correct Answer: C 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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 computes results immediately using eager execution, making it simple to use.
- D. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
Correct Answer: A 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
- A. Same number as the cluster executors
- B. 20
- C. 1
- D. 10
Correct Answer: D 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:
- A. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
- B. It filters the id values before performing the join.
- C. It increases the partition size for df1 and df2.
- D. It ensures that the join happens only when the id values are identical.
Correct Answer: A 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:
The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:
- A. Add the option wholetext=False to the text() function
- B. Add the option lineSep='\n' to the text() function
- C. Add the option lineSep=", " to the text() function
- D. Add the option wholetext=True to the text() function
Correct Answer: D 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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