Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which benefit of Apache Spark is most directly related to its in-memory processing capability?

   • AIt reduces the need for disk I/O during iterative algorithms and interactive queries.
   • BIt automatically scales the cluster based on memory usage.
   • CIt ensures data is always stored in a fault-tolerant manner without replication.
   • DIt allows Spark to run on any hardware without configuration.
   Reveal Solution Discussion Select an answer first
2. 17expert · hard

   A company runs a Spark job that processes a large dataset and writes results to a data lake. The job occasionally fails due to network issues, and when it restarts, it re-processes the entire dataset, causing delays. They want to avoid re-processing all data on failure. Which challenge of Spark does this scenario highlight?

   • APoor fault tolerance
   • BLack of incremental processing
   • CTuning complexity
   • DHigh memory consumption
   Reveal Solution Discussion Select an answer first
4. 18application · medium

   A media company processes video metadata and user engagement logs. They currently use separate systems for batch processing and interactive queries. They want to simplify their architecture and reduce data duplication. Which advantage of Spark would help them achieve this?

   • AUnified analytics platform
   • BAutomatic data replication
   • CNative support for all database engines
   • DBuilt-in data warehouse
   Reveal Solution Discussion Select an answer first
5. 19expert · hard

   A company has a Spark cluster that is underutilized during off-peak hours. They want to reduce costs while maintaining the ability to handle peak workloads. They are considering using a cluster manager that supports dynamic scaling. Which advantage of Spark is most relevant to this cost optimization?

   • AUnified analytics
   • BEase of use
   • CSpeed
   • DScalability
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   Which challenge is commonly associated with running Spark workloads in a production environment?

   • ASpark automatically handles all performance tuning, so no manual intervention is needed.
   • BSpark requires a specific programming language, limiting its adoption.
   • CSpark cannot integrate with existing data storage systems like HDFS or S3.
   • DSpark's in-memory processing can lead to high memory consumption and requires careful resource management.
   Reveal Solution Discussion Select an answer first