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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Handling missing values and data quality - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Partitioning and bucketing data - Joining and combining datasets - Filtering, sorting, and aggregating data |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Spark architecture overview - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Execution and deployment modes |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Identifying performance bottlenecks - Optimizing transformations and actions - Managing memory and resource usage - Debugging and logging |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Running SQL queries - Integrating Spark SQL with DataFrames - Working with functions and expressions |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Structured Streaming | 10% | - Defining streaming queries - Output modes and triggers - Streaming concepts and architecture - Fault tolerance and state management |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?
- A. Increase the number of executor instances to handle more concurrent tasks.
- B. Reduce the size of the data partitions to improve task scheduling.
- C. Increase the executor memory allocation in the Spark configuration.
- D. Set the spark.network.timeout property to allow tasks more time to complete without being killed.
Correct Answer: A 🗳️
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?
- A. spark.createDataFrame(cube_func("num")).show()
- B. spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show() - C. num_df.select(cube_func("num")).show()
- D. num_df.register("cube_func").select("num").show()
Correct Answer: B 🗳️
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.mode("append").partitionBy("country").parquet("/data/output")
- B. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- C. df.write.partitionBy("country").parquet("/data/output")
- D. df.write.mode("overwrite").parquet("/data/output")
Correct Answer: B 🗳️
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)
B)
C)
D)
The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?
- A. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
) - B. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
) - C. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
) - D. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
Correct Answer: C 🗳️
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.json("path/to/file", overwrite=True)
- B. df.write.format("json").save("path/to/file", mode="overwrite")
- C. df.write.mode("overwrite").json("path/to/file")
- D. df.write.overwrite.json("path/to/file")
Correct Answer: C 🗳️
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
