MCQs on Spark on Cloud Platforms | Apache Spark MCQs Questions
This set of Apache Spark MCQs Question is designed to test your knowledge on running Spark on cloud platforms such as AWS, Azure, and GCP. The questions explore key concepts like integrating Spark with cloud storage, autoscaling, cost optimization, and utilizing serverless Spark through Databricks. Whether you are preparing for certifications, interviews, or enhancing your expertise, these questions will help you master the core concepts and practical applications of Apache Spark on cloud services.
Chapter 9: Spark on Cloud Platforms – MCQs
Topic 1: Running Spark on AWS EMR, Azure HDInsight, and GCP
- What is the primary benefit of using AWS EMR for running Spark jobs?
a) Fully managed cloud infrastructure
b) Cost-effective storage options
c) Direct integration with Databricks
d) Dedicated GPU support - Which cloud platform offers the HDInsight service for running Spark?
a) AWS
b) Google Cloud Platform (GCP)
c) Microsoft Azure
d) IBM Cloud - In GCP, which service allows you to run Apache Spark in a fully managed environment?
a) Google Dataproc
b) Google App Engine
c) Google Compute Engine
d) Google Kubernetes Engine - What is a key feature of AWS EMR for Spark workloads?
a) Supports only batch processing
b) Fully automated scaling and management
c) No integration with Hadoop
d) Does not support Spark Streaming - Which Azure service provides a managed environment to run Spark clusters?
a) Azure Kubernetes Service
b) Azure Databricks
c) Azure Blob Storage
d) Azure Synapse Analytics
Topic 2: Integrating Spark with Cloud Storage (S3, Blob, GCS)
- Which cloud storage service is commonly integrated with Apache Spark on AWS?
a) Google Cloud Storage (GCS)
b) Azure Blob Storage
c) Amazon S3
d) Azure Data Lake - What is required to read and write data from S3 using Apache Spark?
a) HDFS connector
b) S3 connector for Spark
c) Azure Data Lake connector
d) GCS connector - Which cloud storage service is integrated with Apache Spark on Azure?
a) Amazon S3
b) Azure Blob Storage
c) Google Cloud Storage
d) Oracle Cloud Storage - How does Apache Spark interact with cloud storage?
a) Through cloud storage APIs
b) By using custom connectors
c) By leveraging the Hadoop Distributed File System (HDFS)
d) All of the above - Which of the following cloud storage services is integrated with Spark on Google Cloud Platform (GCP)?
a) Azure Blob Storage
b) Amazon S3
c) Google Cloud Storage (GCS)
d) Google Drive
Topic 3: Autoscaling and Cost Optimization
- What is the purpose of autoscaling in cloud-based Spark environments?
a) To automatically adjust the number of nodes based on workload
b) To manually adjust the storage capacity
c) To reduce network traffic
d) To optimize the use of APIs - Which of the following is a benefit of using autoscaling for Spark on AWS EMR?
a) Fixed cost regardless of usage
b) Automatically scales based on resource requirements
c) Limited to only batch jobs
d) Does not support Spark Streaming - How does Azure Databricks support cost optimization in Spark workloads?
a) By reducing the size of input data
b) Through automatic cluster resizing and scaling
c) By limiting the number of transformations
d) By reducing network bandwidth - Which feature of AWS EMR helps in cost optimization for Spark jobs?
a) EC2 Spot Instances
b) Reserved instances only
c) Elastic Load Balancing
d) AWS Lambda - What is the main advantage of using serverless Spark environments like Databricks?
a) It reduces the need for cluster management
b) It allows for high customization of Spark configurations
c) It limits Spark’s capabilities
d) It requires manual resource allocation
Topic 4: Serverless Spark with Databricks
- What does “serverless” mean in the context of Apache Spark with Databricks?
a) No need for any clusters or resource management
b) Spark jobs are executed without Spark being installed
c) Serverless execution requires no data storage
d) Serverless environments do not support Spark streaming - Which platform provides a managed Spark environment with serverless capabilities?
a) AWS Lambda
b) Azure Databricks
c) Google Cloud Functions
d) AWS Glue - In a Databricks environment, how are clusters managed in a serverless setup?
a) The user must manually configure and scale clusters
b) Databricks automatically manages clusters without user intervention
c) Serverless setups do not support cluster configurations
d) Databricks only supports manual scaling - What is a major advantage of using Databricks for serverless Spark jobs?
a) Increased network latency
b) Automatic scaling and resource allocation
c) Fixed computational resources
d) High cost for low-volume workloads - Which of the following is a key feature of Databricks for serverless Spark?
a) Requires user-managed Spark configurations
b) Provides managed clusters without manual intervention
c) Requires preconfigured clusters for Spark jobs
d) Only supports batch processing
Topic 5: General Cloud-Based Spark Operations
- What is the benefit of running Spark jobs on cloud platforms?
a) Automatic resource allocation and scaling
b) Increased cost without performance benefits
c) No integration with external services
d) Limited support for real-time streaming - Which feature of cloud-based Spark environments improves fault tolerance?
a) Use of low-cost storage
b) Multi-region replication and backup
c) Serverless execution
d) Manual configuration of resource scaling - How does AWS EMR handle Spark job failures?
a) Restarts jobs on failed nodes
b) Automatically retries failed tasks
c) Stops all running jobs
d) Only logs errors without retrying - Which of the following can lead to higher costs in cloud-based Spark environments?
a) Frequent autoscaling of clusters
b) Idle cluster instances running unnecessarily
c) Low resource utilization during job execution
d) All of the above - Which of the following is a feature of Google Cloud Dataproc for running Spark jobs?
a) Fully managed Hadoop clusters
b) Automatically shuts down clusters after job completion
c) No support for data connectors
d) Requires manual cluster scaling - How can you ensure better performance when running Spark on cloud platforms?
a) By manually scaling the clusters
b) By using cloud-native storage services
c) By avoiding cloud storage services altogether
d) Both a and b - What happens when you run a Spark job on a cloud platform with limited resources?
a) The job runs faster due to resource constraints
b) The job may fail due to insufficient resources
c) The job executes on a single node
d) The job automatically terminates - How do cloud providers charge for running Spark jobs?
a) Based on the number of transformations
b) Based on resource consumption (e.g., CPU, memory, storage)
c) By the number of jobs executed
d) Based on the type of Spark job - What is a benefit of using cloud platforms for Spark jobs compared to on-premise setups?
a) No need for internet connectivity
b) Ability to scale resources as needed
c) Higher upfront costs for infrastructure
d) Limited access to cloud-native services - How does Spark optimize storage when running on cloud platforms like AWS or Azure?
a) By storing all data in memory
b) By utilizing distributed cloud storage services
c) By using single-node processing
d) By storing intermediate data in local disks
Answer Key
| Qno | Answer |
|---|---|
| 1 | a) Fully managed cloud infrastructure |
| 2 | c) Microsoft Azure |
| 3 | a) Google Dataproc |
| 4 | b) Fully automated scaling and management |
| 5 | b) Azure Databricks |
| 6 | c) Amazon S3 |
| 7 | b) S3 connector for Spark |
| 8 | b) Azure Blob Storage |
| 9 | d) All of the above |
| 10 | c) Google Cloud Storage (GCS) |
| 11 | a) To automatically adjust the number of nodes based on workload |
| 12 | b) Automatically scales based on resource requirements |
| 13 | b) Through automatic cluster resizing and scaling |
| 14 | a) EC2 Spot Instances |
| 15 | a) It reduces the need for cluster management |
| 16 | a) No need for any clusters or resource management |
| 17 | b) Azure Databricks |
| 18 | b) Databricks automatically manages clusters without user intervention |
| 19 | b) Automatic scaling and resource allocation |
| 20 | b) Provides managed clusters without manual intervention |
| 21 | a) Automatic resource allocation and scaling |
| 22 | b) Multi-region replication and backup |
| 23 | b) Automatically retries failed tasks |
| 24 | b) Idle cluster instances running unnecessarily |
| 25 | b) Automatically shuts down clusters after job completion |
| 26 | d) Both a and b |
| 27 | b) The job may fail due to insufficient resources |
| 28 | b) Based on resource consumption (e.g., CPU, memory, storage) |
| 29 | b) Ability to scale resources as needed |
| 30 | b) By utilizing distributed cloud storage services |