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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - Partitioning and bucketing data - Joining and combining datasets - Handling missing values and data quality - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Selecting, renaming, and modifying columns - Reading and writing data in various formats |
| Apache Spark Architecture and Components | 20% | - Execution and deployment modes - Spark architecture overview - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection - Shuffling, actions, and broadcasting |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Identifying performance bottlenecks - Managing memory and resource usage - Debugging and logging |
| Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Running SQL queries - Using catalog and metadata APIs - 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% | - Output modes and triggers - Streaming concepts and architecture - Defining streaming queries - Fault tolerance and state management |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
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()
- C. df.select("Name").distinct().orderBy(df["Name"])
- D. df.select("Name").orderBy(df["Name"].asc())
Correct Answer: C 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
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. Broadcast join, as df2 is smaller than the default broadcast threshold
- B. Shuffle join because no broadcast hints were provided
- C. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
- D. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
Correct Answer: A 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?
- A. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
- B. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
- C. regions_dict = regions.select("region_id", "region_name").take(3)
- D. regions_dict = dict(regions.take(3))
Correct Answer: B 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?
- A. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
- B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
- C. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
- D. The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.
Correct Answer: B 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
- A. APPEND
- B. AGGREGATE
- C. COMPLETE
- D. REPLACE
Correct Answer: C 🗳️
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
