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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Structured Streaming | 10% | - Defining streaming queries - Output modes and triggers - Fault tolerance and state management - Streaming concepts and architecture |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Topic 3: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Topic 4: Developing Apache Spark DataFrame API Applications | 30% | - User-defined functions (UDFs) - Reading and writing data in various formats - Creating DataFrames and defining schemas - Joining and combining datasets - Partitioning and bucketing data - Handling missing values and data quality - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data |
| Topic 5: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Debugging and logging - Identifying performance bottlenecks - Optimizing transformations and actions |
| Topic 6: Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Spark architecture overview - Fault tolerance and garbage collection - Execution and deployment modes - Execution hierarchy and lazy evaluation |
| Topic 7: Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Working with functions and expressions - Running SQL queries - Using catalog and metadata APIs |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. 42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts")) \
.withColumn("event_month", F.month("event_ts")) \
.bucketBy(42, ["event_year", "event_month"]) \
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?
A) Add .sortBy() after .bucketBy()
B) Change the bucket count (42) to a lower number
C) Replace .bucketBy() with .partitionBy("event_year", "event_month")
D) Replace .bucketBy() with .partitionBy("event_year") only
2. A developer initializes a SparkSession:
spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?
A) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
D) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
3. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?
A) The operation will load all data into the driver's memory, potentially causing memory overflow
B) Data will be lost during conversion
C) The conversion will automatically distribute the data across worker nodes
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows
4. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?
A) FATAL, NONE, INFO, DEBUG
B) WARN, NONE, ERROR, FATAL
C) ERROR, WARN, TRACE, OFF
D) ALL, DEBUG, FAIL, INFO
5. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:
A) Use the sortBy() transformation to reorganize the data
B) Use the coalesce() transformation with a lower number of partitions
C) Use the repartition() transformation with a lower number of partitions
D) Use the distinct() transformation to combine similar partitions
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |
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