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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Structured Streaming10%- Streaming concepts and architecture
- Defining streaming queries
- Output modes and triggers
- Fault tolerance and state management
Topic 2: Troubleshooting and Tuning Apache Spark DataFrame API Applications10%- Debugging and logging
- Identifying performance bottlenecks
- Optimizing transformations and actions
- Managing memory and resource usage
Topic 3: Developing Apache Spark DataFrame API Applications30%- Reading and writing data in various formats
- Selecting, renaming, and modifying columns
- User-defined functions (UDFs)
- Filtering, sorting, and aggregating data
- Joining and combining datasets
- Creating DataFrames and defining schemas
- Handling missing values and data quality
- Partitioning and bucketing data
Topic 4: Using Spark Connect to Deploy Applications5%- Connecting to remote Spark clusters
- Running applications via Spark Connect
- Spark Connect architecture
Topic 5: Using Spark SQL20%- Using catalog and metadata APIs
- Integrating Spark SQL with DataFrames
- Working with functions and expressions
- Running SQL queries
Topic 6: Using Pandas API on Apache Spark5%- Converting between Pandas and Spark structures
- Key differences and limitations
- Overview of Pandas API on Spark
Topic 7: Apache Spark Architecture and Components20%- Execution and deployment modes
- Fault tolerance and garbage collection
- Execution hierarchy and lazy evaluation
- Shuffling, actions, and broadcasting
- Spark architecture overview

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A broadcast nested loop join
B) A Cartesian join
C) A shuffled hash join
D) A sort-merge join


2. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.format("parquet").load("events.parquet").show()
B) spark.sql("SELECT schema FROM events.parquet").show()
C) spark.read.parquet("events.parquet").printSchema()
D) spark.sql("SELECT * FROM events.parquet").show()


3. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) Data will be lost during conversion
B) The operation will load all data into the driver's memory, potentially causing memory overflow
C) The conversion will automatically distribute the data across worker nodes
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows


4. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Support for SQL queries on structured data
B) In-memory computation and parallel processing capabilities
C) Built-in machine learning libraries
D) Ability to process small datasets efficiently


5. A developer runs:

What is the result?
Options:

A) It stores all data in a single Parquet file.
B) It creates separate directories for each unique combination of color and fruit.
C) It throws an error if there are null values in either partition column.
D) It appends new partitions to an existing Parquet file.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: C
Question # 3
Answer: B
Question # 4
Answer: B
Question # 5
Answer: B

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