Google Professional Machine Learning Engineer Question 162
Single answerGoogle Cloud PlatformYou are a machine learning engineer working on a data pipeline to preprocess large-scale datasets for a recommendation system. The pipeline runs on Dataproc, and you are using PySpark within Jupyter notebooks to interactively develop and test your transformations. However, your data transformations are taking longer than expected, and you suspect inefficient resource utilization. What is the best approach to optimize your Spark job execution in this scenario?
- A
Enable dynamic allocation of executors and adjust partition sizes to balance the workload.
- B
Switch to using BigQuery SQL instead of Spark for data processing.
- C
Increase the memory allocated to the driver to avoid task failures.
- D
Disable caching and re-read the data from the source for every transformation.
Show answer and explanation
Correct answer: A
Explanation
To optimize a Spark job, it is crucial to ensure efficient resource utilization and workload balance across executors. Enabling dynamic allocation allows Spark to scale the resources up or down as needed during job execution. Additionally, adjusting partition sizes ensures that the data is evenly distributed, avoiding bottlenecks due to skewed partitions. These steps directly address the problem of slow data transformations in the given scenario.
- A. Correct.
This is correct. Enabling dynamic allocation of executors allows Spark to adjust the number of executors based on the workload, which can optimize resource utilization. Additionally, adjusting partition sizes can help balance the workload across executors, reducing execution time.
- B. Incorrect.
This is incorrect. While BigQuery is efficient for certain types of queries, the question specifies that Spark is already being used within Dataproc and Jupyter notebooks. Switching to BigQuery would not directly address the inefficiencies in the Spark job.
- C. Incorrect.
This is incorrect. Increasing the memory allocated to the driver may resolve specific memory-related issues, but it does not address the broader problem of inefficient resource utilization in Spark jobs.
- D. Incorrect.
This is incorrect. Disabling caching would result in Spark re-reading the data from the source for every transformation, which would significantly increase processing time and resource usage.