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Exam Code: Associate-Developer-Apache-Spark-3.5
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| DataFrame API with PySpark | - Built-in functions and expressions - Transformations and actions - DataFrame creation and schema management |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Data Processing and Performance | - Caching and persistence strategies - Joins and data partitioning - Optimization techniques |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?
- A. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
- B. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
- C. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
- D. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
Correct Answer: C 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?
- A. Set the number of partitions equal to the total number of CPUs in the cluster
- B. Set the number of partitions equal to the number of nodes in the cluster
- C. Set the number of partitions to a fixed value, such as 200
- D. Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?
- A. The code fails to execute because the column names employee_id and emp_id do not match automatically
- B. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
- C. The code fails to execute because PySpark does not support joining DataFrames with a different structure
- D. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?
- A. Improved job execution speed due to reduced logging overhead
- B. Prevention of driver log accumulation during long-running jobs
- C. Enhanced executor performance due to reduced log size
- D. Loss of access to past job logs and reduced debugging capability for completed jobs
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?
- A. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start() - B. query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start() - C. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start() - D. query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()
Correct Answer: A 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
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