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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Structured Streaming | 10% | - Output modes and triggers - Defining streaming queries - Streaming concepts and architecture - Fault tolerance and state management |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Topic 3: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Debugging and logging - Identifying performance bottlenecks - Managing memory and resource usage |
| Topic 4: Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Joining and combining datasets - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Partitioning and bucketing data - Handling missing values and data quality |
| Topic 5: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Topic 6: Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Working with functions and expressions - Running SQL queries |
| Topic 7: Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Shuffling, actions, and broadcasting - Spark architecture overview - Execution hierarchy and lazy evaluation - Execution and deployment modes |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A stage contains multiple tasks, and each task contains multiple jobs.
- B. A job contains multiple stages, and each stage contains multiple tasks.
- C. A stage contains multiple jobs, and each job contains multiple tasks.
- D. A job contains multiple tasks, and each task contains multiple stages.
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsQuiz members. You can sign-up / login (it's free).
What is the benefit of Adaptive Query Execution (AQE)?
- A. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
- B. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
- C. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
- D. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsQuiz 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 = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record")) - B. exploded_df = exploded_df.select("record_datetime", "record_exploded")
- C. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
- D. 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"))
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsQuiz members. You can sign-up / login (it's free).
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.partitionBy("country").parquet("/data/output")
- B. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- C. df.write.mode("append").partitionBy("country").parquet("/data/output")
- D. df.write.mode("overwrite").parquet("/data/output")
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsQuiz members. You can sign-up / login (it's free).
Given the code:
df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?
- A. When the show action is applied
- B. When the count action is applied
- C. When the filter transformation is applied
- D. When the groupBy transformation is applied
Correct Answer: B 🗳️
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