7.7 AWS AI Service Selection Case Lab

Case method for AWS AI service selection

A service list is easy to memorize and easy to misuse. Case judgment is the skill that matters. A practitioner should read a scenario, identify the outcome, map the data type, decide whether the task is common or custom, check whether probabilistic output is acceptable, and name the AWS service path with boundaries. The strongest answer usually includes what the service does not solve.

Start with the outcome. Does the user need extracted fields, a transcript, a translation, a search result, a generated response, a recommendation, a forecast, a fraud signal, a dashboard, or a custom model? Then identify the modality: document, text, audio, image, video, tabular data, clickstream, logs, or mixed sources. This prevents service names from being chosen because they sound familiar rather than because they fit.

Next, decide whether the task is a managed capability, a foundation model application, or custom ML. If the task is a common AI API, start with a managed service. If the task is language generation, summarization, or question answering over context, start with Amazon Q or Bedrock. If the task is a unique prediction that needs training on company data and lifecycle control, consider SageMaker AI. If the problem is deterministic reporting or workflow routing, avoid unnecessary AI.

Case 1: Accounts payable automation. The business wants vendor name, invoice number, totals, dates, and line items from uploaded invoices. The best first service is Textract, not a general chatbot. The implementation may store files in S3, use validation rules, route exceptions to humans, and publish metrics to QuickSight. A foundation model might later summarize notes, but extraction is the core service-selection signal.

Case 2: Internal policy assistant. Employees ask questions about benefits, travel rules, and IT procedures. Amazon Q Business can be a strong managed assistant fit if documents are current and permissions are respected. A Bedrock RAG application may fit if the team needs a custom app experience, model choice, or specialized guardrails. The practitioner should flag stale content, source ownership, restricted documents, feedback loops, and user training.

Case 3: Contact center improvement. Leaders want better visibility into customer calls and faster after-call work. Transcribe can create call transcripts. Lex may handle simple self-service intents. Comprehend or contact center analytics can help identify topics or sentiment. Bedrock might summarize calls or draft notes. The answer is a workflow, not one magic service. Privacy, recording notice, retention, and review are mandatory questions.

Case 4: Recommendation engine. A streaming service wants personalized content rows. Personalize is the managed recommendation service to evaluate if the company has interaction events, content metadata, and user context. If the catalog is tiny or data is sparse, a simple rules-based list may be better at first. Personalization quality should be judged by business metrics and user feedback, not by whether the service can technically run.

Case 5: High-impact prediction. A lender wants to predict default risk from applicant data. This is not a casual AI use case. A custom ML path might involve SageMaker AI, but the governance bar is high: fairness, explainability, data lineage, monitoring, human review, compliance review, and appeal processes. A practitioner should not recommend a black-box automated decision simply because ML is available.

Use this five-step case lab method:

1. Name the user outcome in one sentence.
2. Identify the input data type and sensitivity.
3. Choose managed AI, foundation model app, custom ML, analytics, or no AI.
4. State the AWS service starting point and the reason it fits.
5. Add the top governance risk, human review need, and operational owner.

Official practice should reinforce this method. Use AWS Skill Builder, official practice question sets, labs, and sandbox console exploration to compare service inputs and outputs. Do not search for exam dumps or supposed live questions. They are unreliable and violate the point of certification study. Scenario practice should teach judgment that transfers to real AWS work.

The final habit is to answer with assumptions. For example: If the task is extracting structured fields from documents, start with Textract. If the task is an employee assistant over governed documents, evaluate Amazon Q Business. If the task is custom prediction from proprietary labels, discuss SageMaker AI readiness. If the task is a simple monthly trend report, use analytics first. That style shows practitioner-level service selection and responsible scope control.