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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Running SQL queries |
| Topic 2: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Topic 3: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Identifying performance bottlenecks - Managing memory and resource usage - Optimizing transformations and actions |
| Topic 4: Structured Streaming | 10% | - Fault tolerance and state management - Defining streaming queries - Streaming concepts and architecture - Output modes and triggers |
| Topic 5: Developing Apache Spark DataFrame API Applications | 30% | - Handling missing values and data quality - Selecting, renaming, and modifying columns - Reading and writing data in various formats - Filtering, sorting, and aggregating data - Partitioning and bucketing data - Joining and combining datasets - Creating DataFrames and defining schemas - User-defined functions (UDFs) |
| Topic 6: Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Overview of Pandas API on Spark - Converting between Pandas and Spark structures |
| Topic 7: Apache Spark Architecture and Components | 20% | - Execution hierarchy and lazy evaluation - Execution and deployment modes - Fault tolerance and garbage collection - Spark architecture overview - Shuffling, actions, and broadcasting |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?
- A. A Cartesian join
- B. A broadcast nested loop join
- C. A shuffled hash join
- D. A sort-merge join
Correct Answer: C 🗳️
Explanation: Only visible for TestsDumps members. You can sign-up / login (it's free).
39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?
- A. Broadcast a variable to share the maximum time among workers.
- B. Use an accumulator to record the maximum time on the driver.
- C. Use an RDD action like reduce() to compute the maximum time.
- D. Configure the Spark UI to automatically collect maximum times.
Correct Answer: C 🗳️
Explanation: Only visible for TestsDumps members. You can sign-up / login (it's free).
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:
def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?
- A. Convert the Pandas UDF from a Series → Series UDF to an Iterator[Series] → Iterator[Series] UDF
- B. Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
- C. Convert the Pandas UDF to a PySpark UDF
- D. Run the in_spanish_inner() function in a mapInPandas() function call
Correct Answer: A 🗳️
Explanation: Only visible for TestsDumps members. You can sign-up / login (it's free).
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:
- A.
- B.
- C.
- D.
Correct Answer: C 🗳️
Explanation: Only visible for TestsDumps 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 = regions.select("region_id", "region_name").take(3)
- B. regions_dict = dict(regions.take(3))
- C. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
- D. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
Correct Answer: D 🗳️
Explanation: Only visible for TestsDumps members. You can sign-up / login (it's free).
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