AI Use Cases You’ll Actually See on the AIF-C01 Exam (and at Work): Vision, NLP, Speech, Recs, Fraud & Forecasting

If you can map a business problem to the right AI pattern (and AWS service), you’ll unlock easy exam points—and build systems people use every day.

Why practical AI use cases matter (for AIF-C01 and real projects)

You know that feeling when you read an exam question and it is basically describing a real job you have seen, just with the company name removed? That is AIF-C01 in a nutshell.

This exam does not usually reward memorizing obscure model math. It rewards pattern recognition. The question is rarely “what is the formula?”, and almost always “what kind of problem is this, and what is the simplest AWS managed approach?”

In real projects, that same skill saves you weeks. If your stakeholder says “we need to stop fake account signups,” you do not want to wander into building a custom deep learning pipeline unless you truly need to. You want to quickly label it as an identity and risk problem, choose a managed service or a light custom workflow, and ship something that works.

So when we talk about computer vision, NLP, speech recognition, recommendations, fraud detection, and forecasting, we are not listing random AI topics. We are naming the repeatable “shapes” of problems that show up in support tickets, product roadmaps, and yes, certification questions.

The practical takeaway: learn to translate business language into AI patterns. Once you can do that, picking an AWS service (or knowing when not to) becomes much more obvious, and the exam starts feeling less like trivia and more like reading comprehension.

And if you are stressed: that is normal. The good news is that there are only a handful of patterns you have to get fluent in, and the rest is just practice.

Core concept in plain language: the “AI pattern → use case → AWS service” mapping

Most “AI use case” questions are really just matching games in disguise.

Think of it like walking into a hardware store. You do not ask for “a metal thing.” You ask for a hammer because you recognize the job: driving nails. AI scenarios are the same. You spot the job, then pick the tool.

Here is the mapping you are building in your head for AIF-C01: AI pattern, use case, AWS managed service.

Start with the pattern. Computer vision means the input is an image or video, and the output is something like labels, faces, or a moderation decision. NLP means the input is text, and the output is things like sentiment, entities, topics, or classification. Speech recognition means the input is audio, and the output is text, usually with timestamps.

Then go one level up into “use case language.” “Find damaged parts on an assembly line” is vision. “Summarize customer feedback themes” is NLP. “Create call transcripts for QA” is speech-to-text. “Show me what else I might like” is recommendations. “Is this transaction suspicious?” is fraud detection. “How many units will we sell next week?” is forecasting.

Finally, connect it to a service. Rekognition is the classic vision answer. Comprehend is the classic NLP answer. Transcribe is the classic speech-to-text answer. Personalize is recommendations. Fraud Detector is fraud scoring. Forecast is time-series forecasting.

Two quick exam-friendly clues.

First, the data type is your biggest hint. Images and video, text, audio, user-item interactions, or time series. If you can identify that in the first 10 seconds, you are already most of the way to the right option.

Second, managed services are the default unless the scenario forces custom. If the question talks about “quickly adding” a capability, “no ML expertise,” or “minimize operational overhead,” it is basically waving a flag that AWS managed AI is the intended answer.

What you need to know (key facts to memorize for the exam)

If you want easy points on AIF-C01, memorize inputs and outputs, not feature trivia.

A fast way to study is to make a tiny flashcard for each pattern that answers three questions: What data goes in? What comes out? What does the business do with it next?

For computer vision, the input is images or video. The output is structured detections like “this contains a person,” “this looks like a delivery truck,” or “this face matches a known profile.” The business action is usually automation, review queues, or safety checks.

For NLP, the input is text from places like emails, chats, reviews, PDFs, and ticket notes. The output is extracted meaning: sentiment, key phrases, entities, topic labels, or a classification label you can route on. In plain English, Amazon Comprehend is a managed service that uses NLP to extract insights about the content of documents, so it is your default answer when the scenario screams “understand text at scale.”

For speech recognition, the input is audio. The output is a transcript, often needed for search, QA, analytics, and compliance. On the exam, watch for “live captions” (streaming) versus “process these recordings overnight” (batch).

For recommendations, the input is interaction data: views, clicks, purchases, ratings, plus item and user metadata if you have it. The output is a ranked list: “show these items next.” The business action is conversion and retention.

For fraud detection, the input is events like logins, account signups, and transactions with context signals (device, IP, location, velocity). The output is a risk score or a decision that triggers step-up verification.

For forecasting, the input is time series: historical demand, inventory levels, web traffic, energy usage. The output is future predictions with confidence bounds that drive staffing, purchasing, and budgeting.

One more thing to memorize: the exam loves “what service do I use?” questions, but in real life you still need the pipeline around it. Think storage, data prep, identity, monitoring, and a human review loop when mistakes are costly.

Real-world AI applications (and the AWS services that fit them)

The easiest way to get good at use cases is to picture a real workflow, not a whiteboard diagram.

Computer vision (Amazon Rekognition): Imagine you run a marketplace and users upload product photos. You want to tag images (“shoe,” “handbag,” “electronics”) to improve search, and you also want basic safety checks to flag inappropriate content for review. That is computer vision: image in, labels and signals out, then either automate or send to a moderator queue. Rekognition is built to add deep learning based image and video analysis to applications, so it is the natural fit when the question says “analyze images” or “analyze video” without asking you to build a custom model stack.

NLP for document understanding (Amazon Comprehend): Now picture a support team drowning in tickets. Customers write in free-form text: “I was charged twice,” “my delivery is late,” “the app keeps crashing.” A practical NLP workflow is to classify each message into a category, detect sentiment so angry customers get routed faster, and extract key entities like order IDs or product names. The point is not that the model is magical. It is that text becomes structured enough for automation.

Healthcare NLP (Comprehend Medical): A common twist is domain language. Clinical notes, discharge summaries, and lab narratives look like English, but they behave like a different dialect full of abbreviations and specialized terms. On the exam, “medical notes” is a keyword that should make you consider the medical variants rather than the general ones.

Speech-to-text (Amazon Transcribe): Picture a contact center. Calls are recorded, and supervisors want to review 1 percent of calls for quality, but also want analytics across 100 percent. Speech recognition turns audio into text so you can search it, measure talk time, and run downstream NLP to detect themes. Amazon Transcribe is an automatic speech recognition service that converts audio to text, which is why it shows up any time you see “transcribe,” “captions,” “call recordings,” or “voice notes.”

Speech in regulated domains (Transcribe Medical): Similar pattern, but higher stakes. If the scenario is clinician dictation or medical conversations, the exam wants you to notice that medical speech has different vocabulary and risk controls. In real projects, it also changes your review and compliance expectations.

Recommendations (Amazon Personalize): Think of the “next best thing” widgets you see everywhere. “Customers who viewed this also viewed,” “because you watched,” “recommended for you.” The input is behavior logs and item catalogs, and the output is a ranked list you can serve in real time. The practical trick is remembering that recommendations are not predictions of the future. They are a sorting problem: what to show first.

Fraud detection (Amazon Fraud Detector): Imagine an ecommerce checkout. Most purchases are boring, but a few are risky. Fraud detection workflows score events using signals like purchase amount, shipping distance, account age, device fingerprint, and how fast the user is trying different cards. The output is typically a score that triggers an action: approve, deny, or step up with MFA.

Forecasting (Amazon Forecast): Finally, the manager question that never goes away: “How many will we need next week?” Forecasting shows up in inventory planning, call center staffing, energy load planning, and marketing spend. The input is time series plus related signals (promotions, holidays, price changes), and the output is a forecast you can plan against.

If you want to get exam-fast, practice turning each scenario into a one-liner: “This is images,” “this is text,” “this is audio,” “this is interactions,” “this is time series.” Then your service choice becomes boringly straightforward.

Exam tips + common mistakes (how to pick the right use case fast)

Most wrong answers happen because people start with the service name instead of the input.

A better approach is almost mechanical. First, identify the input type: image or video, text, audio, interactions, or time series. Second, identify the output the business wants: labels, extracted entities, transcripts, ranked items, risk scores, or forecasts.

Then look for the “tie-breaker” words. Real-time versus batch is a big one (live captions versus transcribe recordings overnight). Domain hints are another (clinical notes, medical dictation). And watch for when the question is really asking for rules, not AI. If they want “if amount is over X and country is Y, block it,” that is business logic, not machine learning.

Common mistake: calling everything “NLP.” If the scenario is audio, you need speech-to-text first, then optionally NLP on the transcript.

Quick recap: data type first, output second, service last. Do that consistently and you will feel the exam speed up.