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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 2: Structured Streaming | 10% | - Streaming Applications
|
| Topic 3: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 4: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 5: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 6: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 7: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
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. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
- C. Shuffle join because no broadcast hints were provided
- D. Broadcast join, as df2 is smaller than the default broadcast threshold
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
- A. spark.conf.set("spark.pandas.arrow.enabled", "true")
- B. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
- C. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
- D. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?
- A. Switch the deployment mode to local mode
- B. Add more executor instances to the cluster
- C. Switch the deployment mode to cluster mode
- D. Increase the driver memory on the client machine
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
- A. 1
- B. Same number as the cluster executors
- C. 20
- D. 10
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?
- A. Collecting persistent table statistics and storing them in the metastore for future use
- B. Optimizing the layout of Delta files on disk
- C. Dynamically switching join strategies
- D. Improving the performance of single-stage Spark jobs
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
