Google Professional Machine Learning Engineer Question 163
Select 3Google Cloud PlatformYou are a Machine Learning Engineer working on a large-scale data preprocessing pipeline for a recommendation system. The dataset resides in Google Cloud Storage (GCS) and consists of terabytes of unstructured data. You decide to use Apache Spark on Dataproc with Jupyter notebooks and a Spark kernel to process the data. Which of the following actions should you take to ensure efficient data processing and compatibility with the Spark kernel?
- A
Use the SparkSession object provided by the kernel to load data directly from GCS.
- B
Leverage Spark's built-in transformations such as map, filter, and reduce to preprocess the data in parallel.
- C
Download the data locally first and then use Spark to process it for faster I/O operations.
- D
Ensure the Spark cluster is configured with sufficient memory and executors to handle the dataset size.
- E
Manually split the dataset into smaller chunks before loading it into Spark to avoid memory issues.
Show answer and explanation
Correct answers: A, B, D
Explanation
Using Spark kernels allows seamless integration with large-scale data processing frameworks like Apache Spark. Leveraging the SparkSession to load data directly from GCS, using built-in transformations for distributed processing, and configuring the cluster appropriately are all best practices for handling large datasets efficiently in this scenario. Avoiding unnecessary local downloads or manual partitioning ensures optimal performance and scalability.
- A. Correct.
Correct: The SparkSession object allows direct interaction with data sources like GCS. Using it ensures compatibility with the Spark kernel and avoids unnecessary data transfers.
- B. Correct.
Correct: Spark's built-in transformations are optimized for distributed data processing and are essential for handling large-scale datasets efficiently.
- C. Incorrect.
Incorrect: Downloading data locally defeats the purpose of distributed processing and will significantly slow down the pipeline when working with huge datasets.
- D. Correct.
Correct: Configuring the cluster with adequate memory and executors ensures that Spark can efficiently distribute and process the dataset without running out of resources.
- E. Incorrect.
Incorrect: Spark is designed to handle large datasets without requiring manual splitting. It automatically partitions data into manageable chunks.