Analytics, AI, Developer and Management Services
Cloud Practitioner service selection includes analytics, AI/ML, developer tools, Systems Manager, CloudFormation, CloudWatch, Trusted Advisor, and support automation.
How to study for AWS Cloud Practitioner
Treat each question as a service-selection or responsibility decision: identify the business need, AWS responsibility, customer configuration, and best-fit service.
Core concepts
Concept 1
Analytics services help collect, process, query, transform, stream, and visualize data.
Exam cue: Use QuickSight for business intelligence dashboards.
Concept 2
AI and ML services provide managed capabilities for language, vision, recommendations, search, forecasting, and generative AI use cases.
Exam cue: Use CloudFormation for infrastructure as code.
Concept 3
Developer and management services automate deployment, configuration, monitoring, operations, and infrastructure as code.
Exam cue: Use Systems Manager for operational management across instances and resources.
Risk pitfalls and guardrails
Choosing a raw compute service when a managed analytics service fits.
Guardrail: Avoid answers that ignore customer configuration, choose unmanaged services when managed services fit, or use billing reports when proactive alerts are required.
Confusing CloudFormation with CloudWatch.
Guardrail: Avoid answers that ignore customer configuration, choose unmanaged services when managed services fit, or use billing reports when proactive alerts are required.
Treating AI output as automatically accurate without validation.
Guardrail: Avoid answers that ignore customer configuration, choose unmanaged services when managed services fit, or use billing reports when proactive alerts are required.
Memory anchors
Athena
Amazon Athena queries data in S3 using SQL without managing servers.
Redshift
Amazon Redshift is a managed data warehouse for analytics.
QuickSight
Amazon QuickSight provides business intelligence dashboards and visualizations.
Kinesis
Amazon Kinesis collects and processes streaming data.
SageMaker
Amazon SageMaker helps build, train, and deploy machine learning models.
Comprehend
Amazon Comprehend uses natural language processing to find insights in text.
Bedrock
Amazon Bedrock provides access to foundation models for generative AI applications.
CloudFormation
AWS CloudFormation provisions infrastructure as code using templates.
Systems Manager
AWS Systems Manager helps manage and automate operations across AWS resources.
CodePipeline
AWS CodePipeline automates release workflows for applications and infrastructure.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A data analyst wants to run ad hoc SQL queries directly against log files stored in Amazon S3 without provisioning a database server. Which service should be used?
A company needs to discover, prepare, and combine data from several sources before analysis, using a serverless data integration service. Which service fits?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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