AWS Certified AI Practitioner Exam Dumps July 2026
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401 questions with answers
Updation Date : 16 Jul, 2026
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Amazon AIF-C01 Sample Questions
Question # 21
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost. Which solution will meet these requirements?
A. Customize the model by using fine-tuning. B. Decrease the number of tokens in the prompt. C. Increase the number of tokens in the prompt. D. Use Provisioned Throughput.
Answer: B ExplanationDecreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.Token Reduction Strategy:By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.Why Option B is Correct:Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.Why Other Options are Incorrect:A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.C. Increase the number of tokens: Would increase costs, not lower them.D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.
Question # 22
A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world. Which principle of responsible Al does the company demonstrate in this scenario?
A. Fairness B. Explainability C. Governance D. Transparency
Answer: A ExplanationThe company is training an image classification model for diagnosing insect bites using a diverse dataset that includes photos from different genders, ethnicities, and geographic locations. This approach demonstrates the principle of fairness in responsible AI, as it aims to reduce bias and ensure the model performs equitably across diverse populations.Exact Extract from AWS AI Documents:From the AWS AI Practitioner Learning Path:"Fairness in AI involves ensuring that models do not exhibit bias against certain groups and perform equitably across diverse populations. This can be achieved by training models on diverse datasets that represent various demographics, such as gender, ethnicity, and geographic location."(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)Detailed Explanation:Option A: FairnessThis is the correct answer. By using a diverse dataset, the company ensures the model is less likely to be biased against specific groups, promoting fairness in its predictions and treatments for insect bites.Option B: ExplainabilityExplainability refers to making the model’s decisions understandable to users, such as byproviding insights into how predictions are made. The scenario focuses on dataset diversity, not explainability.Option C: GovernanceGovernance involves establishing policies and processes to manage AI systems, such as compliance and oversight. The scenario does not describe governance mechanisms.Option D: TransparencyTransparency involves disclosing how a model works, its limitations, and its data sources. While transparency is important, the scenario specifically highlights the diversity of the dataset, which aligns more directly with fairness.References:AWS AI Practitioner Learning Path: Module on Responsible AIAWS Documentation: Responsible AI Principles (https://aws.amazon.com/machine-learning/responsible-ai/)Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-bias.html)
Question # 23
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm. Which type of data will meet this requirement?
A. Text data B. Image data C. Time series data D. Binary data
Answer: C ExplanationAmazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (onedimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.Reference: Amazon SageMaker DeepAR Algorithm
Question # 24
A company wants to control employee access to publicly available foundation models (FMs). Which solution meets these requirements?
A. Analyze cost and usage reports in AWS Cost Explorer. B. Download AWS security and compliance documents from AWS Artifact. C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs. D. Build a hybrid search solution by using Amazon OpenSearch Service.
Answer: C ExplanationThe correct answer is C because Amazon SageMaker JumpStart provides administrative controls that allow organizations to manage and restrict access to foundation models within the AWS environment.According to the official AWS documentation:"Amazon SageMaker JumpStart provides model access management capabilities that enable administrators to control which foundation models are visible and usable by end users. Using AWS Identity and Access Management (IAM) policies, you can restrict access to specific models or completely disable model discovery in JumpStart."This allows companies to enforce governance over which FMs their users can see and interact with, satisfying the requirement to control employee access to publicly available foundation models.Explanation of other options:A. AWS Cost Explorer is used to analyze billing and usage data but does not control access to services or models. It is helpful for budgeting and visibility, not access control.B. AWS Artifact provides access to compliance reports and certifications, not tools for controlling user access to ML models.D. Amazon OpenSearch Service is used for search and analytics on structured and unstructured data. It does not provide access control mechanisms for foundation models.Referenced AWS AI/ML Documents and Study Guides:Amazon SageMaker JumpStart Documentation – Model Access ManagementAWS IAM Documentation – Restricting Access to SageMaker ResourcesAWS Machine Learning Specialty Certification Guide – Security and Governance Section
Question # 25
What does an F1 score measure in the context of foundation model (FM) performance?
A. Model precision and recall. B. Model speed in generating responses. C. Financial cost of operating the model. D. Energy efficiency of the model's computations.
Answer: A ExplanationThe F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.
Question # 26
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms. What should the firm do when developing and deploying the LLM? (Select TWO.)
A. Include fairness metrics for model evaluation. B. Adjust the temperature parameter of the model. C. Modify the training data to mitigate bias. D. Avoid overfitting on the training data. E. Apply prompt engineering techniques.
Answer: A C ExplanationTo implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.A. Include Fairness Metrics for Model Evaluation:Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.C. Modify the Training Data to Mitigate Bias:Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.Why Other Options are Incorrect:B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.
Question # 27
A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
A. Input the prompts into the model. Generate responses by using real-time inference. B. Use Amazon Bedrock batch inference. Generate responses asynchronously. C. Use Amazon Bedrock agents. Build an agent system to process the prompts recursively. D. Use AWS Lambda functions to automate the task. Submit one prompt after another and store each
response.
Answer: B ExplanationThe correct answer is B – Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.Referenced AWS AI/ML Documents and Study Guides:Amazon Bedrock Developer Guide – Batch InferenceAWS ML Specialty Study Guide – Scalable Inference Options
Question # 28
A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task. Which step of the foundation model (FM) lifecycle does this describe?
A. Fine-tuning B. Data selection C. Pre-training D. Evaluation
Answer: A ExplanationFine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.Data selection is about curating training data.Pre-training is the initial training phase on massive datasets.Evaluation happens after training, not during.# Reference:AWS Documentation – Fine-tuning in Amazon Bedrock
Question # 29
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?
A. Data augmentation B. Fine-tuning C. Model quantization D. Continuous pre-training
Answer: B ExplanationFine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model’s parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.Exact Extract from AWS AI Documents:From the AWS Bedrock User Guide:"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks."(Source: AWS Bedrock User Guide, Model Customization)Detailed Explanation:Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.Option C: Model quantizationModel quantization reduces the precision of a model’s weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.References:AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html)AWS AI Practitioner Learning Path: Module on Model Training and CustomizationAmazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
Question # 30
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer. What should the company do to meet these requirements?
A. Evaluate the models by using built-in prompt datasets. B. Evaluate the models by using a human workforce and custom prompt datasets. C. Use public model leaderboards to identify the model. D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
