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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - Selecting, renaming, and modifying columns - Reading and writing data in various formats - Partitioning and bucketing data - Joining and combining datasets - Handling missing values and data quality - Filtering, sorting, and aggregating data - User-defined functions (UDFs) - Creating DataFrames and defining schemas |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Working with functions and expressions - Integrating Spark SQL with DataFrames - Running SQL queries |
| Apache Spark Architecture and Components | 20% | - Execution and deployment modes - Spark architecture overview - Shuffling, actions, and broadcasting - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation |
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Overview of Pandas API on Spark - Key differences and limitations |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Structured Streaming | 10% | - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers - Defining streaming queries |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Optimizing transformations and actions - Managing memory and resource usage - Identifying performance bottlenecks |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
- A. coalesce
- B. filter
- C. select
- D. groupBy
Correct Answer: D 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)
B)
C)
D)
- A. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
- B. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - C. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - D. exploded_df = exploded_df.select("record_datetime", "record_exploded")
Correct Answer: B 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A stage contains multiple jobs, and each job contains multiple tasks.
- B. A job contains multiple tasks, and each task contains multiple stages.
- C. A job contains multiple stages, and each stage contains multiple tasks.
- D. A stage contains multiple tasks, and each task contains multiple jobs.
Correct Answer: C 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?
- A. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
- B. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
- C. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
- D. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
Correct Answer: A 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?
- A. The number of days specified will be added to the start date.
- B. The number of days specified will be removed from the start date.
- C. The same start date will be returned.
- D. An error message of an invalid parameter will be returned.
Correct Answer: A 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
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