Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Execution and deployment modes - Spark architecture overview - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection |
| Topic 2: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Optimizing transformations and actions - Debugging and logging - Identifying performance bottlenecks |
| Topic 3: Structured Streaming | 10% | - Output modes and triggers - Fault tolerance and state management - Streaming concepts and architecture - Defining streaming queries |
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Running applications via Spark Connect - Spark Connect architecture |
| Topic 5: Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Overview of Pandas API on Spark - Converting between Pandas and Spark structures |
| Topic 6: Developing Apache Spark DataFrame API Applications | 30% | - Selecting, renaming, and modifying columns - Handling missing values and data quality - Creating DataFrames and defining schemas - Filtering, sorting, and aggregating data - Reading and writing data in various formats - Partitioning and bucketing data - User-defined functions (UDFs) - Joining and combining datasets |
| Topic 7: Using Spark SQL | 20% | - Using catalog and metadata APIs - Working with functions and expressions - Running SQL queries - Integrating Spark SQL with DataFrames |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?
- A. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
- B. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
- C. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
- D. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
Correct Answer: B 🗳️
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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. In-memory computation and parallel processing capabilities
- B. Built-in machine learning libraries
- C. Ability to process small datasets efficiently
- D. Support for SQL queries on structured data
Correct Answer: A 🗳️
Explanation: Only visible for Lead1Pass 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 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.
- B. 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.
- C. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
- D. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
Correct Answer: B 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
- A. By configuring the option checkpointLocation during readStream
- B. By configuring the option recoveryLocation during writeStream
- C. By configuring the option recoveryLocation during the SparkSession initialization
- D. By configuring the option checkpointLocation during writeStream
Correct Answer: D 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:
The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:
- A. Add the option lineSep=", " to the text() function
- B. Add the option wholetext=False to the text() function
- C. Add the option lineSep='\n' to the text() function
- D. Add the option wholetext=True to the text() function
Correct Answer: D 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
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