The Exam Room

22 Apr 2026 · 16 min read

Exam Room · SAA-C03

The Closest Healthy Region

A multi-region application needs to route requests to the closest healthy region, failing over automatically when the preferred one drops out -- with no client-side retries and no extra health-check plumbing to maintain. Route 53 can do all of that in a single record set. Finding the correct combination means touring all seven routing policies and the attributes that separate them.

Read article 27 Apr 2026 · 15 min read

Exam Room · SAA-C03

The Archive Nobody Reads

Some data exists for compliance, not for use. Tens of terabytes of records sitting untouched until an auditor wants them. S3 has eight storage classes; only one of them is built for that pattern, and getting it wrong can cost an order of magnitude in a year you weren't paying attention to the bill.

Read article 29 Apr 2026 · 17 min read

Exam Room · AIF-C01

Buy, Borrow, Build

A product manager with no ML background has been told to add AI to a SaaS product, and has heard of Bedrock, SageMaker, Comprehend, Translate, Textract, Rekognition. AWS has three different shapes of AI offering, and the shortest path depends entirely on whether a ready-made service already does the job.

Read article 04 May 2026 · 20 min read

Exam Room · AIF-C01

From Raw Model to Production Endpoint

A product team wants a chatbot that summarises support tickets. They have the tickets, a cloud account, and no ML background. Somebody says 'use a foundation model'. Between that sentence and a working endpoint sit roughly seven distinct stages, each with its own AWS service and its own decisions. Picking the model is the easy part; the real work is figuring out which stages this team can skip, which they absolutely cannot, and what AWS gives them at each step.

Read article 06 May 2026 · 19 min read

Exam Room · AIF-C01

Prompt, Retrieve, or Fine-Tune

A legal-ops team wants a model that answers questions about their 4,000 in-house contract templates. The first prototype, a plain Claude call with the question in the prompt, hallucinates clause numbers. Someone suggests fine-tuning; someone else suggests RAG. They solve different problems, so 'which is better' is the wrong frame; what matters is which problem the team actually has, and what each adaptation technique costs in time, data, and recurring spend.

Read article 11 May 2026 · 21 min read

Exam Room · AIF-C01

Grounding a Chatbot in Your Own PDFs

A facilities team has 600 PDFs -- equipment manuals, safety procedures, maintenance schedules -- sitting on a SharePoint drive. Engineers want a chatbot that answers 'how do I reset the chiller on floor 4?' in seconds instead of a ten-minute PDF hunt. Retrieval-augmented generation can do this; whether it does it well depends on what the corpus actually looks like, what kinds of questions the engineers really ask, and which configuration knobs decide whether the answers are any good once a managed service is on the table.

Read article 13 May 2026 · 18 min read

Exam Room · AIF-C01

Forecasting Without Writing Python

A category manager has 18 months of weekly sales data for 400 SKUs and a deadline to forecast next quarter. She doesn't code. The ML team is booked until Q3. The ask is a tool that lets her build a forecast herself -- importable, reviewable, explainable -- without waiting for engineering. Which AWS box she clicks matters less than what kind of problem this actually is, what features of the data can honestly feed into a model, and what the business user has to understand for the output to be defensible when finance asks ''why this number?''.

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