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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| 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:
12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?
- A. filtered_users = raw_users.dropna(how="any")
- B. filtered_users = raw_users.dropna(how="all")
- C. filtered_users = raw_users.na.drop("all")
- D. filtered_users = raw_users.na.drop("any")
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. Increase the number of local threads based on the number of CPU cores.
- C. Set the spark.executor.memory property to a large value.
- D. Configure the application to run in cluster mode instead of local mode.
Correct Answer: B 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)
- A. Only actions trigger the execution of the transformation pipeline.
- B. The Spark engine optimizes the execution plan during the transformations, causing delays.
- C. Transformations are executed immediately to build the lineage graph.
- D. Transformations are evaluated lazily.
- E. The Spark engine requires manual intervention to start executing transformations.
Correct Answer: A,D 🗳️
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A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:
Which of the following code snippets will read all the data within the directory structure?
- A. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
- B. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
- C. df = spark.read.parquet("/path/events/data/*")
- D. df = spark.read.parquet("/path/events/data/")
Correct Answer: A 🗳️
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A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)
Which join strategy will Spark use?
- A. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
- B. Broadcast join, as df2 is smaller than the default broadcast threshold
- C. Shuffle join because no broadcast hints were provided
- D. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
Correct Answer: B 🗳️
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