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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Running SQL queries |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Developing Apache Spark DataFrame API Applications | 30% | - Creating DataFrames and defining schemas - Selecting, renaming, and modifying columns - Handling missing values and data quality - User-defined functions (UDFs) - Joining and combining datasets - Reading and writing data in various formats - Partitioning and bucketing data - Filtering, sorting, and aggregating data |
| Structured Streaming | 10% | - Streaming concepts and architecture - Output modes and triggers - Fault tolerance and state management - Defining streaming queries |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Spark architecture overview - Execution and deployment modes |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Identifying performance bottlenecks - Managing memory and resource usage - Optimizing transformations and actions - Debugging and logging |
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question 1
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
A. Increase the value of spark.sql.shuffle.partitions
B. Increase the size of the dataset to create more partitions
C. Enable dynamic resource allocation to scale resources as needed
D. Reduce the value of spark.sql.shuffle.partitions
Question 2
A developer runs:
What is the result?
Options:
A. It appends new partitions to an existing Parquet file.
B. It creates separate directories for each unique combination of color and fruit.
C. It throws an error if there are null values in either partition column.
D. It stores all data in a single Parquet file.
Question 3
23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)
A. It processes data solely on disk storage, reducing the need for memory resources.
B. It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
C. It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
D. It eliminates the need to write any code, automatically handling all data processing.
E. It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
Question 4
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?
A. 
result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
B. 
result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))
C. 
result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
D. 
result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
Question 5
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?
A. Data will be lost during conversion
B. The conversion will automatically distribute the data across worker nodes
C. The operation will fail if the Pandas DataFrame exceeds 1000 rows
D. The operation will load all data into the driver's memory, potentially causing memory overflow
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: C,E | Question 4 Answer: D | Question 5 Answer: D |
