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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Debugging and logging - Optimizing transformations and actions - Identifying performance bottlenecks |
| Topic 2: Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Reading and writing data in various formats - Partitioning and bucketing data - Selecting, renaming, and modifying columns - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Handling missing values and data quality - Filtering, sorting, and aggregating data |
| Topic 3: Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Running SQL queries - Integrating Spark SQL with DataFrames |
| Topic 4: Apache Spark Architecture and Components | 20% | - Execution hierarchy and lazy evaluation - Shuffling, actions, and broadcasting - Spark architecture overview - Execution and deployment modes - Fault tolerance and garbage collection |
| Topic 5: Structured Streaming | 10% | - Defining streaming queries - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers |
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Spark Connect architecture - Running applications via Spark Connect |
| Topic 7: Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
A) spark.conf.set("spark.sql.execution.arrow.enabled", "true")
B) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
C) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
D) spark.conf.set("spark.pandas.arrow.enabled", "true")
2. What is the benefit of using Pandas on Spark for data transformations?
Options:
A) It computes results immediately using eager execution, making it simple to use.
B) It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
C) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D) It is available only with Python, thereby reducing the learning curve.
3. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:
Which operation is supported with streamingdf ?
A) streaming_df.groupby("Id") .count ()
B) streaming_df.filter (col("count") < 30).show()
C) streaming_df.orderBy("timestamp").limit(4)
D) streaming_df. select (countDistinct ("Name") )
4. 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("purchase_date", current_date()).where("total_purchase > 50")
B) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
C) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
5. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
A) REPLACE
B) AGGREGATE
C) COMPLETE
D) APPEND
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: C |
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