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AWS AI Practitioner study materials
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AWS AI Practitioner practice questions

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Question 1Fundamentals of AI and ML

A company wants a system that can identify the sentiment of customer reviews without any labeled training examples. Which approach best fits this requirement?

  1. Applying k-means clustering to the review text
  2. Training a supervised classifier on labeled reviews
  3. Using a pretrained foundation model with zero-shot prompting Correct answer.
  4. Building a decision tree from historical ratings

Explanation

Zero-shot prompting lets a pretrained foundation model perform a task from instructions alone, without task-specific labeled data. Supervised training and decision trees require labeled examples, so they do not satisfy the no-label constraint.

Question 2Fundamentals of AI and ML

Which statement best describes the relationship between artificial intelligence, machine learning, and deep learning?

  1. The three terms describe identical techniques and are interchangeable
  2. Machine learning is a subset of deep learning, which is a subset of artificial intelligence
  3. Deep learning is a subset of machine learning, which is a subset of artificial intelligence Correct answer.
  4. Artificial intelligence is a subset of machine learning, which is a subset of deep learning

Explanation

Artificial intelligence is the broad field of building systems that perform tasks associated with human intelligence. Machine learning is one approach within AI, and deep learning is a specialized subset of machine learning using multi-layer neural networks.

Question 3Fundamentals of AI and ML

A data scientist splits a dataset into training, validation, and test sets. What is the primary purpose of the validation set?

  1. To provide the final unbiased estimate of model performance
  2. To store outliers that are excluded from the training process
  3. To tune hyperparameters and compare model variants during development Correct answer.
  4. To increase the total volume of data available for gradient updates

Explanation

The validation set is used during development to select hyperparameters and compare candidate models. The test set, kept untouched until the end, provides the final estimate of generalization performance.

Question 4Fundamentals of AI and ML

Which type of machine learning uses labeled input-output pairs to learn a mapping from features to a target?

  1. Unsupervised learning
  2. Self-supervised pretraining
  3. Reinforcement learning
  4. Supervised learning Correct answer.

Explanation

Supervised learning trains on examples where the correct output is provided, allowing the model to learn a mapping from inputs to targets. Unsupervised and self-supervised methods do not rely on explicit human-provided labels for the target task.

Question 5Fundamentals of AI and ML

What is the main advantage of using a managed AI service such as Amazon SageMaker instead of building training infrastructure from scratch?

  1. It eliminates the need to prepare or clean training data
  2. It automatically guarantees high accuracy on any dataset
  3. It removes the need to choose an algorithm or model type
  4. It reduces undifferentiated infrastructure work so teams can focus on models Correct answer.

Explanation

Managed AI platforms handle provisioning, scaling, and operational tasks so practitioners can concentrate on data and modeling. They do not remove the need for data preparation, algorithm selection, or accuracy validation.

Question 6Fundamentals of AI and ML

A model performs very well on its training data but poorly on new data. Which term describes this behavior?

  1. Underfitting
  2. Data drift
  3. Overfitting Correct answer.
  4. Feature engineering

Explanation

Overfitting occurs when a model captures noise and specifics of the training set instead of general patterns, so performance degrades on unseen data. Underfitting is the opposite problem, where the model is too simple to fit even the training data well.

Question 7Fundamentals of AI and ML

Which task is a classic example of unsupervised learning?

  1. Estimating a reward function from agent actions
  2. Predicting house prices from labeled sales records
  3. Grouping customers into segments based on purchase behavior without labels Correct answer.
  4. Classifying emails as spam or not spam using labeled examples

Explanation

Customer segmentation via clustering is unsupervised because the algorithm discovers groupings without predefined labels. Price prediction and spam classification are supervised tasks that rely on labeled targets.

Question 8Fundamentals of AI and ML

What does the term inference mean in the context of a deployed machine learning model?

  1. Updating model weights using a training dataset
  2. Measuring the size of the training dataset
  3. Converting raw data into numerical features
  4. Using a trained model to generate predictions on new inputs Correct answer.

Explanation

Inference is the process of running a trained model on new inputs to produce predictions or outputs. Training, by contrast, is the phase in which model weights are learned from data.

Question 9Fundamentals of AI and ML

An ML team wants to reduce the risk of bias in a credit-scoring model. Which practice most directly addresses this during development?

  1. Increasing the number of training epochs until loss converges
  2. Removing all features related to income and employment
  3. Evaluating model outcomes across demographic groups and remediating disparities Correct answer.
  4. Deploying the model to production to observe real-world results

Explanation

Bias mitigation requires measuring performance and outcomes across subgroups and then correcting disparities through data or modeling changes. Simply training longer or deploying early does not address fairness concerns.

Question 10Fundamentals of AI and ML

Which metric is most appropriate for evaluating a regression model that predicts delivery times in minutes?

  1. Root mean squared error Correct answer.
  2. Precision
  3. F1 score
  4. Area under the ROC curve

Explanation

Regression models predict continuous values, and root mean squared error measures the average magnitude of prediction error in the target's units. ROC AUC, F1, and precision are classification metrics that assume discrete class labels.

Question 11Fundamentals of AI and ML

A team needs a model that can answer questions about internal documents and cite the source passages. Which technique is designed for this?

  1. Fine-tuning an image classifier on document scans
  2. Retrieval-augmented generation that fetches relevant passages before answering Correct answer.
  3. Training a reinforcement learning agent on user clicks
  4. Clustering documents by length

Explanation

Retrieval-augmented generation combines a search step over a document store with a generative model, allowing answers grounded in retrieved passages. This supports source citation and reduces unsupported generation.

Question 12Fundamentals of AI and ML

What is the primary role of a loss function during model training?

  1. To measure the throughput of the inference endpoint
  2. To select which features are removed from the dataset
  3. To store the training dataset in compressed form
  4. To quantify prediction error so optimization can adjust model parameters Correct answer.

Explanation

A loss function assigns a numeric penalty to the difference between predictions and targets. Optimization algorithms minimize this loss by adjusting model parameters during training.

Question 13Fundamentals of AI and ML

A company must keep sensitive patient data on premises but wants to use a cloud-based foundation model. Which approach best satisfies both needs?

  1. Use a private deployment or a service that processes data within a controlled environment Correct answer.
  2. Upload the full patient records to the model provider for fine-tuning
  3. Use the model only for public marketing content
  4. Abandon AI and rely on manual review

Explanation

Private deployment or services that keep data within a controlled environment allow the organization to use foundation models while maintaining data residency and confidentiality. Uploading sensitive records to a third party or avoiding AI entirely does not balance both requirements.

Question 14Fundamentals of AI and ML

Which statement accurately distinguishes generative AI from traditional predictive ML?

  1. Generative AI only classifies inputs, while predictive ML only creates new content
  2. Generative AI requires no training data, while predictive ML always requires labeled data
  3. Generative AI cannot be used for text, only for numeric forecasting
  4. Generative AI creates new content such as text or images, while predictive ML estimates labels or values Correct answer.

Explanation

Generative models learn data distributions and produce new content, whereas traditional predictive models output a label, class, or numeric estimate. Both families rely on training data and can be applied to text or numeric domains.

Question 15Fundamentals of AI and ML

A recommendation team notices that their model keeps recommending only popular items, reducing discovery for users. Which concept does this illustrate?

  1. Feature leakage
  2. Overfitting to the test set
  3. Feedback loop and popularity bias Correct answer.
  4. Gradient vanishing

Explanation

Recommending popular items increases their engagement, which feeds back as training signal and further amplifies their popularity. This self-reinforcing cycle is a feedback loop that produces popularity bias.

Question 16Fundamentals of AI and ML

Which AWS service provides a fully managed environment for building, training, and deploying machine learning models?

  1. Amazon SageMaker Correct answer.
  2. Amazon CloudWatch
  3. AWS Identity and Access Management
  4. Amazon Route 53

Explanation

Amazon SageMaker is a managed platform that covers the machine learning lifecycle, including data preparation, training, tuning, and deployment. The other services provide monitoring, access control, and DNS rather than ML workflows.

Question 17Fundamentals of AI and ML

A team wants to improve a model's performance by creating new input variables from raw data, such as extracting day-of-week from a timestamp. What is this practice called?

  1. Cross-validation
  2. Model quantization
  3. Feature engineering Correct answer.
  4. Regularization

Explanation

Feature engineering is the process of using domain knowledge to derive new input variables that make patterns easier for a model to learn. Regularization, quantization, and cross-validation serve different purposes in training and evaluation.

Question 18Fundamentals of AI and ML

Which scenario is the best fit for reinforcement learning rather than supervised learning?

  1. Translating documents using paired source and target sentences
  2. Detecting fraudulent transactions using labeled chargebacks
  3. Predicting tomorrow's temperature from historical sensor readings
  4. Teaching an agent to navigate a maze through trial and error with rewards Correct answer.

Explanation

Reinforcement learning suits sequential decision problems where an agent learns from rewards and penalties through interaction. The other scenarios involve labeled examples and are better handled by supervised methods.

Question 19Fundamentals of AI and ML

What does the F1 score balance in a binary classification problem?

  1. Training time and inference latency
  2. Precision and recall Correct answer.
  3. Bias and variance
  4. Model size and accuracy

Explanation

The F1 score is the harmonic mean of precision and recall, giving a single number that balances false positives and false negatives. This makes it useful when classes are imbalanced and both error types matter.

Question 20Fundamentals of AI and ML

A team is choosing between a large foundation model and a small task-specific model for a high-volume, low-latency classification task with abundant labeled data. Which choice is generally most appropriate?

  1. An unsupervised clustering model, because labels are abundant
  2. The large foundation model, because larger models are always more accurate
  3. The small task-specific model, because it can be cheaper and faster while meeting accuracy needs Correct answer.
  4. A reinforcement learning agent, because it learns without labels

Explanation

When abundant labeled data exists and latency and cost are critical, a compact task-specific model often delivers sufficient accuracy at lower cost and latency. Large foundation models are better suited to open-ended or low-data tasks.

Question 21Fundamentals of AI and ML

A model trained on historical loan approvals shows lower approval rates for a protected group. Investigation finds the training labels reflect past human decisions. Which root cause best explains this outcome?

  1. The model has too few parameters to learn the pattern
  2. The training data encodes historical human bias that the model learns to reproduce Correct answer.
  3. The validation set was too small to detect accuracy
  4. The inference endpoint is under-provisioned

Explanation

When labels are derived from past human decisions, the model learns and perpetuates the biases present in those decisions. This is a data-provenance problem rather than a capacity, infrastructure, or validation-size issue.

Question 22Fundamentals of AI and ML

A company wants its chatbot to answer only from an approved knowledge base and refuse unrelated questions. Which combination best achieves this?

  1. Fine-tuning on unrelated internet text to broaden knowledge
  2. Reducing the context window to a single token
  3. Retrieval-augmented generation with guardrails that restrict scope Correct answer.
  4. Increasing the model temperature and removing system instructions

Explanation

Retrieval-augmented generation grounds responses in approved documents, while guardrails constrain the model to the intended scope and block out-of-domain requests. Higher temperature or broader fine-tuning would increase, not reduce, off-topic responses.

Question 23Fundamentals of AI and ML

An ML engineer notices that a deployed model's accuracy degrades gradually over months even though the code has not changed. Which explanation is most likely?

  1. The training set has grown larger automatically
  2. The model weights are being corrupted by inference calls
  3. The loss function has been silently replaced
  4. Data drift has shifted the input distribution away from training data Correct answer.

Explanation

Data drift occurs when the statistical properties of real-world inputs change over time, so patterns learned during training no longer match production data. This causes gradual accuracy decline without any code change.

Question 24Fundamentals of AI and ML

A team must decide whether to fine-tune a foundation model or use prompt engineering for a new text-classification task with only a few hundred labeled examples. Which reasoning is soundest?

  1. Neither approach can work without millions of labeled examples
  2. Fine-tuning is always preferred because it uses more compute
  3. Prompt engineering with a pretrained model is a reasonable first step given the small dataset Correct answer.
  4. The team should train a model from scratch to guarantee control

Explanation

With limited labeled data, prompt engineering or few-shot prompting on a pretrained model is a low-cost first step that often performs well. Fine-tuning becomes more attractive as labeled data and the need for specialization grow.

Question 25Fundamentals of AI and ML

Which statement best captures why human oversight remains important in high-stakes AI systems?

  1. Models can reflect training-data limitations and should be reviewed before consequential decisions Correct answer.
  2. Models are guaranteed to be correct once accuracy exceeds ninety percent
  3. Oversight is unnecessary when the model is hosted on managed services
  4. Human review is only needed for image models

Explanation

Models inherit limitations and biases from their training data and can fail in unexpected ways, so human review is essential for consequential decisions. High aggregate accuracy does not guarantee correctness for every case or subgroup.

Question 26Fundamentals of AI and ML

In supervised learning, what is the essential role of labeled training data?

  1. It supplies input-output pairs so the model can learn a mapping that generalizes to new inputs. Correct answer.
  2. It defines a reward signal that the agent maximizes through trial and error.
  3. It stores the final model weights so predictions can be served without retraining.
  4. It provides unlabeled examples so the model can discover hidden clusters on its own.

Explanation

Supervised learning learns a function from inputs to known target outputs, so each training example must pair features with the correct label. The goal is generalization to unseen inputs, not memorization of the training set.

Question 27Fundamentals of AI and ML

A hospital wants an AI system to flag suspicious regions in radiology images and also show which pixels drove each flag so radiologists can verify the reasoning. Which concept does the second requirement describe?

  1. Data augmentation
  2. Model interpretability Correct answer.
  3. Batch normalization
  4. Hyperparameter tuning

Explanation

Interpretability concerns how clearly a human can understand why a model produced a given output. Highlighting influential input regions makes the model's reasoning inspectable, which supports trust and review in clinical settings.

Question 28Fundamentals of AI and ML

A team needs to split data for a model that predicts next month's sales, and the newest months are the most representative of future conditions. Which splitting strategy is most appropriate?

  1. Randomly shuffle all rows and assign 80 percent to training and 20 percent to testing.
  2. Train on earlier periods and test on later periods in chronological order. Correct answer.
  3. Train on later periods and test on earlier periods to check historical accuracy.
  4. Use the same rows for both training and testing to maximize measured accuracy.

Explanation

For time-dependent data, the test set should mimic the future the model will face, so training on past periods and testing on later ones reflects realistic deployment. Random shuffling can leak future information into training and inflate results.

Question 29Fundamentals of AI and ML

A model must assign each incoming support ticket to exactly one of five categories, and every ticket already has one correct category recorded. Which learning setup fits?

  1. Clustering
  2. Regression
  3. Binary classification
  4. Multiclass classification Correct answer.

Explanation

The task predicts one label out of five mutually exclusive categories from labeled examples, which is multiclass classification. Binary classification would apply only with two possible labels.

Question 30Fundamentals of AI and ML

A data scientist wants to estimate how well a trained model will perform on unseen data before deployment, using data the model has never learned from. Which practice provides this estimate?

  1. Deploying the model and waiting for customer complaints
  2. Increasing the number of training epochs until training loss approaches zero
  3. Evaluating the model on a held-out validation or test set Correct answer.
  4. Removing outliers from the training data before fitting

Explanation

A held-out set is not used during training, so its error approximates generalization performance on new data. Training-set metrics tend to be optimistic and cannot reveal overfitting.

Question 31Fundamentals of AI and ML

A bank wants to know which customer attributes most influenced a declined loan application so staff can explain the decision to the applicant. Which capability is needed?

  1. Feature importance or local explanation of the prediction Correct answer.
  2. Automated hyperparameter search
  3. Data labeling through crowdsourcing
  4. Horizontal scaling of inference endpoints

Explanation

Explaining an individual decision requires attributing the output to input features, which feature importance or local explanation methods provide. Operational techniques such as scaling or tuning do not reveal reasons for a specific outcome.

Question 32Fundamentals of AI and ML

A company has a small labeled dataset of product reviews and wants to know whether a simple model is learning real patterns or just memorizing. Training accuracy is 99 percent while validation accuracy is 62 percent. What does this gap most likely indicate?

  1. The labels in the training data are all correct
  2. The model is overfitting the training data Correct answer.
  3. The model is underfitting the training data
  4. The validation set is too large to be useful

Explanation

A large gap with near-perfect training accuracy and much lower validation accuracy is the classic signature of overfitting, where the model captures noise specific to the training rows. Underfitting would show poor accuracy on both sets.

Question 33Fundamentals of AI and ML

Which description correctly separates a parameter from a hyperparameter in a neural network?

  1. Parameters control only the learning rate, while hyperparameters are the network weights.
  2. Parameters are set by the engineer before training, while hyperparameters are learned from data.
  3. Parameters and hyperparameters are both learned automatically and differ only in name.
  4. Parameters are learned from data during training, while hyperparameters are set before training to control the process. Correct answer.

Explanation

Weights and biases are parameters adjusted by the optimization algorithm using training data. Hyperparameters, such as learning rate, batch size, and layer count, are chosen beforehand and govern how training proceeds.

Question 34Fundamentals of AI and ML

A team must choose an evaluation metric for a fraud detector where missing a fraudulent transaction is far costlier than flagging a legitimate one for review. Which metric should they prioritize?

  1. Precision for the fraud class
  2. Overall accuracy
  3. Mean squared error
  4. Recall for the fraud class Correct answer.

Explanation

Recall measures the share of actual fraud cases that were caught, directly reflecting the cost of missed fraud. Accuracy can look high while missing most fraud when fraud is rare, and precision mainly reflects false alarms.

Question 35Fundamentals of AI and ML

An engineer must explain why a spam filter's output changes when one word is replaced, and wants a method that perturbs inputs and observes output changes without inspecting model internals. Which category of technique applies?

  1. White-box gradient inspection
  2. Model-agnostic explanation methods Correct answer.
  3. Synthetic minority oversampling
  4. Grid search cross-validation

Explanation

Methods that probe a model by altering inputs and observing outputs work regardless of the model's internal structure, so they are model-agnostic. They are useful when the model is a black box or access to internals is restricted.

Question 36Fundamentals of AI and ML

A team must decide whether a candidate model is ready for deployment. They want a single procedure that repeatedly resamples the training data, trains the model on each split, and reports the spread of validation scores. Which approach matches this requirement?

  1. k-fold cross-validation Correct answer.
  2. Confusion matrix analysis on the training set
  3. A single train-test split
  4. Principal component analysis

Explanation

k-fold cross-validation rotates which fold is held out, producing multiple validation scores whose spread indicates stability and sensitivity to the data split. A single split gives only one estimate and no measure of variability.

Question 37Fundamentals of AI and ML

A retail team has millions of unlabeled transaction records and wants to group customers by purchasing behavior without predefined segments. Which approach fits, and why?

  1. Binary classification, because each customer is either a buyer or not
  2. Clustering, because it discovers structure in data without predefined labels Correct answer.
  3. Reinforcement learning, because the model learns from customer rewards
  4. Regression, because it predicts a numeric segment value for each customer

Explanation

Clustering is an unsupervised technique that groups similar records based on patterns in the features, requiring no predefined segment labels. The absence of labels rules out supervised classification and regression.

Question 38Fundamentals of AI and ML

Which statement best describes why foundation models are often described as general-purpose compared with traditional task-specific models?

  1. A foundation model can only ever produce numeric regression outputs.
  2. A foundation model is trained separately from scratch for every downstream task it supports.
  3. A foundation model requires no data at all to perform a new task.
  4. A single pretrained foundation model can be adapted to many downstream tasks through prompting or fine-tuning. Correct answer.

Explanation

Foundation models are pretrained on broad data and then adapted to many tasks, which is what makes them general-purpose. Task-specific models are typically trained for one narrow objective from the start.

Question 39Fundamentals of AI and ML

A team is selecting an algorithm for a tabular dataset with a nonlinear decision boundary and limited training time. They want a model that builds an ensemble of decision trees and usually performs well with little tuning. Which choice fits best?

  1. A rule-based expert system
  2. A gradient-boosted tree ensemble Correct answer.
  3. Linear regression
  4. k-means clustering

Explanation

Tree ensembles capture nonlinear relationships and interactions in tabular data and are known for strong performance with modest tuning effort. Linear regression assumes a linear relationship, and k-means is unsupervised.

Question 40Fundamentals of AI and ML

A product team wants to add a feature that suggests a likely next word as a user types, and they have a large corpus of unlabeled text but no annotated examples. Which learning approach fits this situation?

  1. Unsupervised clustering, where words are grouped by frequency and each cluster becomes a suggestion
  2. Supervised learning, where each sentence must first be labeled by a human with its intended meaning
  3. Reinforcement learning, where the model receives a reward each time a user accepts a suggestion
  4. Self-supervised learning, where the model learns by predicting parts of the text from the rest of the text Correct answer.

Explanation

Self-supervised learning creates its own labels from the structure of unlabeled data, for example hiding the next word and asking the model to predict it. This lets a model learn language patterns from raw text without human annotation, which matches a next-word suggestion feature. The other approaches either require labels that do not exist here or do not produce a predictive language model.

Question 41Fundamentals of Generative AI

Which statement best describes a foundation model in generative AI?

  1. A small model trained only on one company's private documents
  2. A rule-based system that generates responses from hand-written templates
  3. A large model pretrained on broad data that can be adapted to many downstream tasks Correct answer.
  4. A statistical model used only for ranking search results

Explanation

Foundation models are trained on very large, broad datasets and learn general patterns that transfer to many tasks. Because of this generality, they can be adapted through prompting or fine-tuning instead of being built from scratch for each use case.

Question 42Fundamentals of Generative AI

A team wants a model to summarize customer emails in a specific house style. Which approach most directly teaches the model that style using labeled example pairs?

  1. Switching to a smaller tokenizer
  2. Fine-tuning on curated input-output examples Correct answer.
  3. Increasing the temperature setting
  4. Reducing the context window

Explanation

Fine-tuning adjusts model weights using labeled examples so the model learns a desired style or task behavior. Providing many curated input-output pairs lets the model internalize the target format and tone beyond what a prompt alone conveys.

Question 43Fundamentals of Generative AI

In a retrieval-augmented generation (RAG) system, what is the primary role of the retrieval component?

  1. To compress the model weights for cheaper inference
  2. To score the fluency of the generated response
  3. To fetch relevant source passages that are inserted into the prompt Correct answer.
  4. To generate the final answer without any external data

Explanation

RAG separates knowledge lookup from generation: a retriever finds relevant passages from an external store and passes them into the prompt. The generator then conditions its answer on those passages, which grounds responses in current or private data.

Question 44Fundamentals of Generative AI

Which term describes the tendency of a language model to produce confident but factually incorrect statements?

  1. Hallucination Correct answer.
  2. Latency
  3. Tokenization
  4. Overfitting

Explanation

Hallucination refers to generated content that sounds plausible but is not supported by facts or source data. It happens because models predict likely text rather than verifying truth, so grounding techniques and evaluation are used to reduce it.

Question 45Fundamentals of Generative AI

What does a token represent in the context of large language models?

  1. A permission grant in an identity policy
  2. A cached copy of a previous model response
  3. A single byte of network traffic between client and server
  4. A unit of text, such as a word piece or character sequence, processed by the model Correct answer.

Explanation

Models do not read raw characters; text is split into tokens that may be whole words, word pieces, or characters. Token counts drive context limits and usage billing, so understanding them matters for prompt design and cost.

Question 46Fundamentals of Generative AI

Which capability is characteristic of diffusion models rather than autoregressive language models?

  1. Iteratively removing noise to produce an image Correct answer.
  2. Encrypting data at rest with customer keys
  3. Parsing SQL queries against a relational database
  4. Predicting the next token in a sequence

Explanation

Diffusion models learn to reverse a noising process, starting from random noise and progressively denoising toward a coherent output such as an image. Autoregressive models instead generate one token at a time conditioned on prior tokens.

Question 47Fundamentals of Generative AI

A developer sets the temperature parameter to a very low value. What is the most likely effect on generated text?

  1. More deterministic and repeatable outputs Correct answer.
  2. Lower token cost per request
  3. Longer maximum output length
  4. More random and diverse outputs

Explanation

Temperature scales the randomness of sampling: lower values concentrate probability on high-likelihood tokens, producing steadier, more repeatable text. Higher values flatten the distribution and increase variety.

Question 48Fundamentals of Generative AI

Which practice best reduces the risk of exposing sensitive data when using a hosted generative AI service?

  1. Increasing the model temperature for anonymity
  2. Disabling logging on the client application only
  3. Sending full production records to improve answer quality
  4. Redacting or excluding sensitive fields before sending prompts Correct answer.

Explanation

Data minimization is the core control: only send what the task requires and remove personal or confidential fields beforehand. This limits what the provider could retain or inadvertently expose, independent of sampling settings.

Question 49Fundamentals of Generative AI

A support team wants answers grounded in the latest internal policy documents without retraining the model each time a policy changes. Which design fits best?

  1. Use retrieval-augmented generation over an indexed policy store Correct answer.
  2. Fine-tune the model weekly on the full policy corpus
  3. Shorten the system prompt to save tokens
  4. Raise the top-p value so the model recalls policies

Explanation

RAG keeps knowledge in an external index that can be updated independently of model weights, so policy edits take effect after reindexing. This avoids repeated, costly fine-tuning cycles and keeps answers traceable to sources.

Question 50Fundamentals of Generative AI

An application must classify incoming tickets into one of five fixed categories. Which prompting approach is most appropriate?

  1. Open-ended creative writing prompt with high temperature
  2. Prompt that asks the model to invent new categories
  3. Structured prompt that lists the categories and requests one label Correct answer.
  4. Prompt that requests a long free-form essay about the ticket

Explanation

Classification benefits from constrained instructions that enumerate the allowed labels and specify the output format. Low randomness and explicit categories make the result easier to validate and parse programmatically.