Databricks Machine Learning Associate Question 492
Select 3You are tasked with preparing a dataset for a machine learning project on Databricks. The dataset contains missing values in several columns, and some columns have inconsistent data types. Additionally, you need to ensure that your data processing pipeline is efficient and scalable. Which of the following steps should you include in your pipeline?
- A
Use Databricks Delta to handle missing values and manage data versioning.
- B
Apply a UDF (User-Defined Function) to transform all numeric columns.
- C
Leverage built-in PySpark functions for data type casting and transformations.
- D
Cache intermediate DataFrames to reduce repeated computation during the pipeline.
- E
Manually inspect each column and fix data inconsistencies using hard-coded logic.
Show answer and explanation
Correct answers: A, C, D
Explanation
Efficient and scalable data processing in Databricks requires leveraging tools and practices that optimize distributed computation. Databricks Delta, built-in PySpark functions, and caching are essential components of such pipelines, while UDFs and manual corrections can introduce inefficiencies or errors.
- A. Correct.
Databricks Delta provides an efficient way to handle missing data and supports versioning, which is crucial for scalable and repeatable pipelines.
- B. Incorrect.
Using UDFs can be less efficient compared to built-in PySpark functions, as UDFs often run slower and are harder to optimize.
- C. Correct.
Built-in PySpark functions are optimized for distributed computation and are preferred for tasks like data type casting and transformations.
- D. Correct.
Caching intermediate DataFrames is a best practice to avoid redundant computations, especially in iterative processes.
- E. Incorrect.
Manually inspecting and fixing data inconsistencies is impractical and error-prone for large datasets, making it unsuitable for scalable pipelines.