AWS AI Practitioner Practice Exam.
Free practice test — 35 verified questions, instant feedback.

Answer: A
Machine learning is the field of building systems that learn statistical patterns from data to make predictions, rather than following hard-coded rules. RPA automates repetitive tasks via scripted rules, not learned patterns. A rule-based expert system relies on explicit if-then logic written by humans. Database indexing is a storage/retrieval optimization technique, unrelated to prediction from patterns.

Answer: B
Supervised learning trains on labeled input-output pairs (email text mapped to a spam/not-spam label), which matches this scenario exactly. Unsupervised learning works with unlabeled data to find structure like clusters. Reinforcement learning learns via rewards from an agent interacting with an environment, not static labels. 'Semi-structured clustering' is not a standard ML paradigm.

Answer: B
Weights scale the influence of each input on a neuron's activation and are iteratively updated during training (e.g., via backpropagation) to minimize error. They do not store training data, do not fix the network's layer count (that's architecture), and are distinct from the learning rate, which is a separate hyperparameter controlling how much weights change per update.

Answer: C
Pre-trained models accessed via API let teams ship quickly without collecting/labeling data or training infrastructure, but the tradeoff is reduced control over the model's internals, biases, and customization versus a model trained in-house. No pre-trained model 'guarantees' superiority in all cases, using an API doesn't remove the need to validate inputs, and using a pre-trained model specifically avoids needing to label large training sets, the opposite of option D.

Answer: B
Overfitting occurs when a model memorizes noise and specific patterns in training data rather than learning generalizable relationships, causing strong training performance but weak generalization to new data. Underfitting is the opposite problem, poor performance on both training and new data because the model is too simple. Data augmentation and feature engineering are techniques used to improve models, not descriptions of this failure mode.

Answer: B
With heavy class imbalance, a model can achieve high raw accuracy by simply always predicting the majority class, so precision, recall, and F1 give a more meaningful view of performance on the minority (fraud) class. Mean squared error and R-squared are regression metrics used for continuous-value prediction, not classification.

Answer: B
Foundation models are pretrained on massive, diverse datasets and are designed to be adapted (via prompting, fine-tuning, or other techniques) to a wide variety of downstream tasks, unlike traditional models built and trained narrowly for a single task. They are typically large, not small, are not limited to image classification, and usually still benefit from prompting or adaptation to perform well on specific tasks.

Answer: A
Autoregressive generation describes producing output sequentially, where each new token is predicted based on the tokens generated so far, which is how most LLMs generate text. Batch normalization is a training-stabilization technique for neural network layers. Federated learning is a distributed training approach across decentralized data. Dimensionality reduction reduces the number of features in data, unrelated to sequential text generation.

Answer: B
In NLP, tokenization splits text into smaller units called tokens (which can be words, subwords, or characters) that a language model processes numerically. This is distinct from authentication tokens (a security concept), hardware bandwidth measures, or annotation labels, which are unrelated concepts that happen to share terminology in other contexts.

Answer: B
Temperature controls the randomness of token sampling; higher temperature flattens the probability distribution over next tokens, producing more varied and creative but less predictable output, while low temperature makes output more deterministic and focused. Temperature does not cause refusals and has no effect on context window size, which is a separate architectural limit.

Answer: B
Few-shot prompting provides a handful of example input-output pairs within the prompt itself to guide the model's response format and behavior without updating model weights. Zero-shot prompting gives no examples at all. Fine-tuning actually updates model weights using a training dataset, which is a different, more resource-intensive process. Model distillation compresses a large model into a smaller one, unrelated to prompting technique.

Answer: A
Hallucination refers to a generative model producing plausible-sounding but factually incorrect or fabricated content. Regularization is a training technique to reduce overfitting. Vectorization converts data (e.g., text) into numeric vector form. Quantization reduces numeric precision of model weights to shrink model size, none of which describe fabricated outputs.

Answer: A
Fine-tuning updates model weights using a labeled dataset so the desired tone/structure becomes part of the model's learned behavior, which is well suited to a durable, large-scale example set. Adjusting temperature only changes randomness, not learned style. Reducing token limits per request restricts response length, unrelated to style. Switching to a smaller model does not by itself encode brand-specific tone.

Answer: B
Prompt engineering influences output purely through how a request is worded or structured at inference time, leaving the underlying model weights untouched, whereas fine-tuning actually retrains and updates the model's parameters using additional data. Option A reverses the definitions. They are not the same process. Fine-tuning, not prompt engineering, is the one that typically requires a labeled dataset.

Answer: A
RAG retrieves relevant external documents at query time and supplies them as context to the model, which is ideal for grounding answers in current or proprietary information not present in the model's training data. Free-form fiction generation has no such grounding need. RAG does not reduce hosting hardware requirements or compress model weights, those relate to infrastructure sizing and quantization respectively.

Answer: A
A vector database stores numeric embeddings representing the semantic meaning of documents and supports similarity search, so the most relevant chunks can be retrieved and passed to the model as context, a core piece of RAG pipelines. It does not replace the foundation model's generation step, is not an authentication store, and does not perform text-to-image conversion.

Answer: B
RAG grounds responses in retrieved, current product data at query time, directly reducing hallucination risk for frequently changing information without needing to retrain the model. Raising temperature increases randomness/creativity, which would worsen hallucination risk. Limiting response length and using a smaller model do not address factual grounding at all.

Answer: A
Tool use (function calling) lets a foundation model decide when to invoke external APIs or functions, passing structured arguments, so an application can trigger real actions like inventory checks based on model output. Quantization is a model-compression technique, batch normalization and gradient clipping are neural network training techniques, none of which relate to invoking external functions from a model.

Answer: A
Agentic AI systems use a foundation model as a reasoning engine to plan multi-step actions, invoke tools, observe results, and adjust course, which goes beyond a single prompt-in/response-out chatbot interaction. It is not just a larger context window, it still relies on a foundation model for reasoning, and it is not limited to image generation.

Answer: B
For open-ended generation tasks like summarization, quality is subjective and multi-dimensional, so human evaluation or LLM-as-judge scoring against criteria such as coherence and factual consistency is far more meaningful than exact string matching, which fails when many valid summaries exist. Latency and token count measure performance/cost, not output quality.

Answer: A
Prompt-based adaptation (zero-shot or few-shot) needs no additional labeled training set or model retraining, so it is much faster and cheaper to experiment with when data or budget is limited. It does not universally outperform fine-tuning (which can outperform prompting for well-defined tasks with enough data), does not alter model weights at all, and requires no access to the original training dataset.

Answer: A
Fairness is the responsible-AI principle concerned with ensuring a model does not produce systematically biased or discriminatory outcomes across demographic groups, which is exactly the hiring-bias risk described. Latency optimization and model compression are performance/engineering concerns unrelated to bias. Autoregressive decoding is a generation mechanism, not a fairness consideration.

Answer: A
Explainability refers to the capacity to articulate, in human-understandable terms, the reasoning or factors behind a model's decision, which is critical for high-stakes use cases like loan approvals where affected individuals and regulators may need justification. Processing speed, cost, and parameter count are performance/scale metrics unrelated to interpretability of decisions.

Answer: A
In high-stakes domains like healthcare, minimizing hallucinated or unverifiable content and ensuring traceability to source data is critical because incorrect summaries can lead to patient harm. Increasing creativity via higher temperature would increase, not reduce, hallucination risk. Reducing human review and avoiding audit logging both increase risk rather than support responsible deployment.

Answer: A
Publishing documentation of intended use, limitations, training data, and evaluation results is a transparency practice, often implemented as 'model cards,' that helps downstream users understand appropriate and inappropriate uses of a model. Data encryption and load balancing are security/infrastructure practices, and gradient descent is a training optimization algorithm, none of which describe documentation practices.

Answer: A
Content filtering and guardrail systems are specifically designed to detect and block toxic, offensive, or otherwise harmful generated content before it reaches users, directly targeting this risk. Context window size affects how much input the model can process, not content safety. Switching to a cheaper inference tier is a cost decision, and evaluation question counts relate to testing methodology, neither mitigates toxic output.

Answer: A
Access control (authorization) governs which authenticated users or roles are permitted to perform specific actions, such as invoking sensitive tools connected to an AI application, which is exactly the scenario described. Quantization is a model-compression technique, data augmentation expands training datasets, and gradient clipping stabilizes training, none of which relate to restricting who can invoke a function.

Answer: A
Prompt injection is an attack where crafted input attempts to override or bypass a model's system instructions or safety constraints, causing it to behave in unintended ways such as leaking restricted data. Model distillation compresses a model into a smaller one, gradient descent is a training optimization method, and data normalization is a data preprocessing step, none of which describe an adversarial manipulation of prompts.

Answer: A
Data governance practices, including isolating tenant data, enforcing access boundaries, and testing whether fine-tuned models leak memorized training examples, directly address the risk of one customer's data surfacing in another's outputs. Temperature controls randomness, context window size limits input length, and parameter count relates to model capacity, none of which prevent data leakage across customers.

Answer: A
Comprehensive logging and audit trails that capture inputs, outputs, and relevant context allow an organization to reconstruct and review AI-driven decisions after the fact, which is essential for regulatory audits. Higher temperature affects creativity/randomness, minimizing training data size would likely hurt model quality, and disabling human review removes oversight rather than supporting auditability.

Answer: A
Encryption at rest protects stored data from unauthorized access if storage is compromised, and encryption in transit protects data moving across networks from interception, together forming a baseline security control regardless of data type or model modality. Encryption has no effect on model accuracy, does not replace the separate need for access control, and applies equally to text, image, or any other data type.

Answer: A
AI governance encompasses the policies, roles, and processes an organization puts in place to oversee responsible development and use of AI systems, including defining accountability for review, monitoring, and incident response. Tokenization and embedding generation are technical NLP/data-representation steps, and backpropagation is a neural network training algorithm, none of which relate to organizational accountability structures.

Answer: A
When real-world data characteristics differ from the training distribution (distribution shift), a model's learned patterns may no longer apply, causing systematically poor predictions in the new context, exactly as described for a new housing market. Parameter count is unrelated to this issue. Hallucination is a term specific to generative models fabricating content, not a regression model's prediction error, and tokenizers are a text-preprocessing component, not applicable to a numeric housing-price prediction failure.

Answer: A
Multimodal foundation models are specifically designed to accept and reason across multiple data types, such as combining an image and a text question to produce a text answer, which matches the described use case. A relational database performs structured data queries, not visual question answering. A text-only model cannot process image input at all, and a hand-coded decision tree lacks the learned representations needed to interpret images and free-form text jointly.

Answer: A
Provisioned or dedicated capacity typically involves a fixed cost in exchange for guaranteed throughput and often lower per-unit cost at high, steady volumes, making it more economical for consistently heavy workloads. For prototypes, rare/unpredictable usage, or one-off demos, low-commitment pay-per-request pricing is generally more cost-effective since there is no sustained volume to justify a fixed capacity commitment.