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lock_openFree sampleGenerative AI fundamentalsmedium
A support team sends the same prompt to a hosted LLM many times a day and gets noticeably different wording each time. They want answers to stay closely on-message and vary far less, without demanding byte-for-byte identical output. Which change best achieves this?
ALower the temperature toward 0.2 so the model concentrates on the highest-probability tokens.check_circle Correct
BRaise the temperature toward 1.0 so the model considers a wider spread of next tokens.
CIncrease the maximum output-token limit so each answer has room to finish completely.
DRaise top-p toward 1.0 so the model samples from the full probability distribution.
Lowering temperature concentrates output on the most-probable tokens, reducing run-to-run variation. Temperature scales how sharply the model favours its most-probable tokens. Lowering it toward 0 concentrates probability mass on the top tokens, so the same prompt produces closely similar, on-message wording each time without forcing fully identical output.
Why A is correct: Correct. A low temperature sharpens the distribution toward the likeliest tokens, so wording stays consistent run to run while still allowing slight variation.
Why B is wrong: Higher temperature flattens the token distribution and increases variation, which moves away from the on-message consistency the team asked for.
Why C is wrong: The token limit governs how long an answer can be, not how much its wording varies, so consistency is unchanged.
Why D is wrong: A top-p of 1.0 keeps the entire distribution in play, which preserves or increases variation rather than reducing it.
lock_openFree sampleFundamentals of AI and MLeasy
A retail company wants a system that improves its product recommendation accuracy over time by learning from customer purchase history, without being explicitly programmed with new rules each season. Which AI discipline best describes this approach?
ARule-based expert systems, which encode domain knowledge as hand-crafted if-then rules.
BMachine learning, which trains models on historical data so that predictive behaviour improves with experience.check_circle Correct
CBusiness intelligence reporting, which aggregates and visualises historical sales data for human analysts.
DRobotic process automation, which executes predefined workflow steps to mimic human repetitive tasks.
Understand that machine learning is the AI discipline in which models improve from data without explicit rule updates. Machine learning algorithms discover patterns in training data and adjust internal parameters so that predictions become more accurate as more data is seen. This contrasts with rule-based systems, which require a human to codify every new condition. The self-improving property from historical purchase data is the hallmark of ML.
Why A is wrong: Tempting because rule-based systems do drive recommendations, but they require explicit programming of every new rule. They do not learn autonomously from data, which is the defining requirement in the stem.
Why B is correct: Machine learning is the discipline in which algorithms learn patterns from data and improve performance on a task without being explicitly re-programmed, matching the scenario precisely.
Why C is wrong: Tempting because it uses historical purchase data, but BI reporting surfaces information for human decision-making rather than training a model that autonomously improves its own predictions.
Why D is wrong: Tempting because RPA is often grouped with AI tooling, but it follows a fixed script and does not learn from data. It cannot improve recommendation accuracy through experience.
lock_openFree sampleApplications of foundation modelsmedium
A developer is building a customer-support chatbot that must answer questions about a company's product catalogue, which is updated weekly. The catalogue contains roughly 200,000 tokens of text. Which design consideration most directly determines whether a single foundation model call can process the entire catalogue without retrieval augmentation?
AThe model's training data cutoff date, because any catalogue content newer than the cutoff will be ignored by the model during inference.
BThe model's output token limit, because generating long catalogue summaries requires a high output ceiling to avoid truncation.
CThe model's context window size, because the full catalogue must fit within the input limit for a single inference call.check_circle Correct
DThe model's temperature setting, because a lower temperature ensures the model attends to all catalogue entries rather than sampling selectively.
Understand that a model's context window size is the primary architectural constraint when deciding whether retrieval augmentation is needed. Foundation models have a fixed context window - the maximum number of tokens they can process in a single inference call, covering both input and output. When source material exceeds this window, the application must use retrieval-augmented generation (RAG) or chunking strategies to surface relevant content before the model call. Training cutoff, temperature, and output limits are real model properties but do not govern input capacity.
Why A is wrong: Training cutoff affects factual knowledge baked into weights, not the ability to process input tokens supplied at inference time. Providing the catalogue as context bypasses the cutoff problem entirely, so this is not the limiting factor here.
Why B is wrong: Output token limits affect response length, not input capacity. The question asks about processing the catalogue as input, so output limits are irrelevant to this constraint.
Why C is correct: Context window defines the maximum tokens a model can accept per call. If the catalogue exceeds that limit, the model cannot process it in one shot, making retrieval augmentation necessary.
Why D is wrong: Temperature controls output randomness and has no bearing on how many input tokens the model can accept. It is a tempting distractor because 'attending to all entries' sounds plausible, but attention and context length are separate concepts.
The AWS Certified AI Practitioner (AIF-C01) exam has 65 questions and runs for 90 minutes. The format is multiple choice and multiple response.
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