Microsoft DP-200 Implementing an Azure Data Solution 05 31Oct2024 - Practice Quiz

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A data engineer is tasked with optimizing a Spark job that processes large datasets stored in Azure Data Lake Storage (ADLS). The job currently uses a single executor with limited resources, leading to long processing times. The engineer decides to implement a more efficient approach by utilizing Spark\'s built-in capabilities for partitioning and parallel processing. Which strategy should the engineer adopt to enhance the performance of the Spark job while ensuring that data is processed efficiently?

Increase the number of partitions for the input data to allow for better parallelism and resource utilization.

Reduce the number of partitions to minimize overhead and improve task scheduling.

Use a single executor with more memory to handle the entire dataset in one go.

Implement a caching mechanism to store intermediate results in memory without adjusting the partitioning strategy.

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