MCQs on Integration with AWS Services | AWS Redshift Integration Questions
Dive into the essential aspects of AWS Redshift integration through this carefully curated set of MCQ questions and answers. Covering topics like Redshift with AWS Glue, QuickSight, Kinesis, and SageMaker, these questions will help you assess and expand your understanding of Redshift’s powerful integrations.
Chapter 9: Integration with Other AWS Services
1-10: Redshift Integration with AWS Glue
- Which AWS service simplifies ETL processes for Redshift?
a) AWS Lambda
b) AWS Glue
c) AWS DataSync
d) AWS Step Functions - What is the purpose of Glue Catalog in Redshift?
a) Monitoring Redshift clusters
b) Storing metadata
c) Encrypting data in transit
d) Scaling compute nodes - Which Redshift feature allows querying Glue data catalogs directly?
a) Spectrum
b) Aqua
c) Workload Management
d) Elastic Resize - AWS Glue supports which programming languages for ETL scripts?
a) Python and Scala
b) JavaScript and C++
c) Ruby and PHP
d) Go and Kotlin - How can you automate ETL jobs between Glue and Redshift?
a) Using CloudWatch Alarms
b) Configuring Glue Triggers
c) Setting up IAM Roles
d) Deploying AWS Beanstalk - Glue’s connection to Redshift requires what key component?
a) VPC Endpoints
b) JDBC URL
c) Kinesis Streams
d) S3 Buckets - Glue’s transformation scripts are stored in:
a) Redshift Storage
b) CloudFormation Templates
c) S3 Buckets
d) Elastic Block Store - Which Glue feature aids schema discovery for Redshift?
a) DataBrew
b) Crawler
c) Athena Queries
d) Redshift Query Editor - How can you optimize Redshift ETL performance via Glue?
a) Increasing Glue worker nodes
b) Reducing partition size
c) Using uncompressed data
d) Disabling Glue triggers - What type of data can Glue and Redshift integrate?
a) Semi-structured and structured
b) Unstructured only
c) Real-time streams only
d) Video files only
11-20: Redshift and Amazon QuickSight
- Amazon QuickSight is primarily used for:
a) Data visualization
b) Data encryption
c) Workflow management
d) Machine learning training - QuickSight connects to Redshift using:
a) APIs
b) JDBC/ODBC drivers
c) VPC Endpoints
d) Lambda Layers - What is SPICE in Amazon QuickSight?
a) A storage engine
b) A performance accelerator
c) A security layer
d) A backup service - Which QuickSight feature directly integrates with Redshift?
a) In-memory processing
b) Live queries
c) Data encryption
d) Query optimization - What must be configured for QuickSight to access Redshift data securely?
a) IAM Roles
b) Security Groups
c) AWS Direct Connect
d) CloudFormation Templates - QuickSight supports which data visualization formats?
a) Bar charts, scatter plots, and pie charts
b) Only bar charts
c) Only pivot tables
d) Text-based insights only - How can you improve Redshift queries in QuickSight?
a) Use materialized views
b) Add more Redshift clusters
c) Enable Redshift Aqua
d) Switch to S3 as the source - QuickSight’s dataset preparation supports:
a) Filtering and joining Redshift tables
b) Compressing Redshift data
c) Replicating Redshift clusters
d) Encrypting QuickSight dashboards - Which user-level feature is critical for QuickSight-Redshift integration?
a) Row-level security
b) Static IP assignment
c) Service quotas
d) Monitoring alarms - QuickSight dashboards can retrieve data from Redshift using:
a) Direct Connect
b) VPC Peering
c) Direct queries or SPICE storage
d) Athena queries
21-30: Streaming Data and Machine Learning Integration
21-25: Streaming Data with Kinesis
21. Kinesis Firehose delivers data to Redshift using:
a) Batch mode
b) Real-time ingestion
c) Push notifications
d) Query execution
- Which service pre-processes streaming data for Redshift?
a) Kinesis Data Analytics
b) CloudTrail
c) AWS Batch
d) Glue Studio - To optimize Kinesis streaming for Redshift, you should:
a) Use Redshift COPY command
b) Increase node size
c) Reduce table constraints
d) Use uncompressed files - Kinesis stream data to Redshift must be:
a) Encrypted with KMS
b) Stored in CloudTrail
c) Filtered through CloudWatch Logs
d) Converted to JSON - How does Kinesis Firehose integrate with Redshift?
a) By storing intermediate data in S3
b) Using Lambda for ETL
c) Through DataSync jobs
d) Directly transferring logs
26-30: Machine Learning Integration with SageMaker
26. SageMaker integrates with Redshift for:
a) Model training and predictions
b) Data encryption
c) Workflow automation
d) Database management
- What command exports Redshift data to SageMaker?
a) UNLOAD
b) EXPORT TO SAGEMAKER
c) COPY INTO SAGEMAKER
d) REDSHIFT UNLOAD - Redshift data can be used in SageMaker via:
a) S3 storage
b) Glue crawlers
c) Kinesis streams
d) CloudWatch metrics - To deploy SageMaker models on Redshift, you should use:
a) Amazon Redshift ML
b) AWS Step Functions
c) AWS Batch
d) Amazon Polly - Redshift ML uses SageMaker for:
a) Training models and applying predictions within SQL
b) Encrypting Redshift clusters
c) Scaling compute nodes
d) Query optimization
Answer Key
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) AWS Glue |
| 2 | b) Storing metadata |
| 3 | a) Spectrum |
| 4 | a) Python and Scala |
| 5 | b) Configuring Glue Triggers |
| 6 | b) JDBC URL |
| 7 | c) S3 Buckets |
| 8 | b) Crawler |
| 9 | a) Increasing Glue worker nodes |
| 10 | a) Semi-structured and structured |
| 11 | a) Data visualization |
| 12 | b) JDBC/ODBC drivers |
| 13 | b) A performance accelerator |
| 14 | b) Live queries |
| 15 | a) IAM Roles |
| 16 | a) Bar charts, scatter plots, and pie charts |
| 17 | a) Use materialized views |
| 18 | a) Filtering and joining Redshift tables |
| 19 | a) Row-level security |
| 20 | c) Direct queries or SPICE storage |
| 21 | b) Real-time ingestion |
| 22 | a) Kinesis Data Analytics |
| 23 | a) Use Redshift COPY command |
| 24 | d) Converted to JSON |
| 25 | a) By storing intermediate data in S3 |
| 26 | a) Model training and predictions |
| 27 | a) UNLOAD |
| 28 | a) S3 storage |
| 29 | a) Amazon Redshift ML |
| 30 | a) Training models and applying predictions within SQL |