Pull out your Class 9 project logbook, and look at the six stages you mapped, in order. You know them: Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment. Each stage has a defined output. The next stage needs that output before it can begin.
The Class 10 form adds something the stage names do not show: two planned switchbacks, and the condition that triggers each. The first returns from Evaluation to Data Acquisition. This switchback is taken when the model's accuracy cannot improve with the data already collected. The second returns from Modeling to Data Exploration. This one is taken when the chosen features do not explain the pattern.
At Class 9, naming the six stages in order was the required form. At Class 10 the board also tests each switchback: its direction, and the reason it is taken. Quick check: which stage do you return to when accuracy cannot improve? Which stage do you return to when the features do not explain the pattern? If you can answer both without looking, you are ready for the one-mark items.
Look at your logbook again. Under Stage 2, Data Acquisition, did you write "whose data?" Under Stage 6, Deployment, did you write "who acts on this output?" Most Class 9 entries do not have these notes. Those questions were not on the Class 9 syllabus. They are on this one.
A second layer of decisions sits inside the cycle. The six stage names never name it directly: ethical checkpoints, at exactly two stages. At Data Acquisition, the checkpoint asks whose data is being collected, and with what consent. At Deployment, it asks who acts on the output, and whether they are equipped to question it.
Each stage name defines a technical output. It does not say who is affected by what was collected, or who acts on what was deployed. A team can finish all six stages without asking the checkpoint questions. That team has done the technical work — and may have skipped the harder decisions. This is the whole Class 10 increment to the cycle: not a new stage, but a second reading of two stages you already know.
You hold your phone to your face and it opens. The camera captured pixels, and a Computer Vision step ran on them. That is the whole domain — a gesture you make every day without naming it. CV is the domain whose AI step looks at pixels and returns a label or a location.
Four CV tools are already in daily use. Face unlock matches facial geometry against a stored template. A QR or barcode scanner reads the grid pattern from the image. Medical-image screening flags a region as normal or not. A licence-plate reader finds the plate, then reads its characters. In each case the step takes an image and returns a structured result. That result is a match, a decoded string, a class, or a position.
When CV fails, it fails in one specific way. It returns the wrong label with high confidence and shows no sign of doubt. The phone may let the wrong person in; the screening tool may flag a healthy region. The CV failure signature is confident, wrong, and silent about it — the model never says, "I am not sure."
The subtitles on a video appear while the speaker is still mid-sentence — word by word, no human typist involved. That is a Natural Language Processing step running. Most people have watched it hundreds of times without naming it. NLP is the domain whose AI step reads human language and returns a structured output.
Four NLP tools show up in daily life. Auto-generated subtitles turn speech into text to display. A voice assistant turns speech into intent, then action. A machine-translation tool turns text in one language into text in another. Keyword tags in a news app turn article text into topic labels. In each case the step takes language — spoken or written — and turns it into something a system can act on.
When NLP fails, the output sounds right but means something wrong. A translation can be a correct sentence in the target language and still carry the wrong meaning. This is harder to catch than CV's failure. The CV output looks wrong — the door stays shut when it should open. The NLP output sounds fine. That is the trap: a fluent answer that is quietly incorrect.
A streaming app suggested a video. It did not read your mind. Someone built a table of watch history, an AI step read that table, and the output became the thumbnail at the top of your screen.
That step is DS — Data Science, or Decision Systems. It is the domain where the AI reads a table of features and returns one number or one category. A person then acts on what it returns.
Four tools you already know work this way. A recommendation engine turns watch history into your next pick. A weather forecast turns past sensor data into tomorrow's conditions. A student-performance predictor turns marks and attendance into a risk flag. A fraud detector turns transaction data into flag or pass.
When DS fails, the output still looks reasonable. It returns a plausible number built from a flawed table.
Consider the student-performance predictor. It may flag students with low attendance. But low attendance can reflect a part-time job, not weak ability. The table recorded what happened. It did not record why.
This is the quietest failure signature of the three domains. No wrong face, no garbled subtitle — just a believable number, with the wrong assumption buried in the table that produced it.
Think about a voice assistant. A user speaks a command. The device recognises their face, parses the words, and ranks a set of responses — one product, three domain steps running in sequence.
It is easy to assume a real AI product uses one domain. That means assuming it is either a CV product, an NLP product, or a DS product. Most real products combine domains in a single pipeline. The voice assistant uses NLP to read the request. It uses CV to recognise the speaker's face, and DS to pick the best response.
The three domains are technical roles within a system, not walls between products. The useful skill is naming which step belongs to which domain. Image to label is CV. Language to structure is NLP. Table to decision is DS. The skill is not deciding which single domain the whole product "is." Thinking the domains cannot mix is the mistake. Knowing which step is which is the readiness.