AWS  AIF-C01 Certified AI Practitioner Exam Questions 2025

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Our AIF-C01 Exam Questions provide authentic and up-to-date questions for the AWS Certified AI Practitioner certification, carefully reviewed by certified experts. Each set comes with verified answers, clear explanations for correct and incorrect options, and references to help you strengthen your understanding. With access to our online exam simulator and free demo questions, Cert Empire makes it easier to prepare with confidence and pass your AWS AIF-C01 exam on the first attempt.

Exam Questions

Question 1

A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers. After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers. How can the company improve the performance of the chatbot?
Options
A: Use few-shot prompting to define how the FM can answer the questions.
B: Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
C: Change the FM inference parameters.
D: Clean the research paper data to remove complex scientific terms.
Show Answer
Correct Answer:
Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
Explanation
The foundation model (FM) is underperforming because it lacks familiarity with the specialized vocabulary ("complex scientific terms") of the research domain. Domain adaptation fine-tuning is the process of further training a pre-trained model on a specific, unlabeled dataset—in this case, the research papers. This process adjusts the model's internal parameters (weights) to learn the nuances, terminology, and statistical patterns of the scientific domain. This directly addresses the root cause of the problem by enhancing the model's core comprehension of the specialized content, leading to significantly improved performance.
References

1. AWS Documentation: The Amazon Bedrock User Guide explains that fine-tuning adapts a model for specific tasks or domains. It states, "Fine-tuning is the process of taking a pre-trained foundation model (FM) and further training it on your own dataset... to make it more specialized for your specific application."

Source: Amazon Bedrock User Guide, "Custom models," section on "Fine-tuning."

2. University Courseware: Stanford University's course on Large Language Models distinguishes between in-context learning (prompting) and fine-tuning. Fine-tuning modifies the model's weights to specialize it, which is necessary when the task requires deep domain knowledge that cannot be conveyed in a few examples.

Source: Stanford University, CS324: Large Language Models, Winter 2022, Lecture 3: "Capabilities," section on "Adaptation."

3. Academic Publication: A foundational paper on language models explains that fine-tuning is a critical step for adapting large pre-trained models to specific downstream tasks or domains, which significantly improves performance over using the base model alone.

Source: Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Section 4: "Experiments." (https://doi.org/10.18653/v1/N19-1423)

Question 2

A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur. Which solution meets these requirements?
Options
A: Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.
B: Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.
C: Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
D: Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.
Show Answer
Correct Answer:
Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
Explanation
Guardrails for Amazon Bedrock is a purpose-built feature designed to implement safeguards in generative AI applications. It allows organizations to define policies to control conversation topics and filter content based on specific criteria, including the removal of Personally Identifiable Information (PII). This directly addresses the requirement to prevent the model from including personal patient data. When a guardrail policy is violated, Amazon Bedrock integrates with Amazon EventBridge (formerly CloudWatch Events), which can capture these violation events. These events can then be used to trigger Amazon CloudWatch alarms or send notifications via services like Amazon Simple Notification Service (SNS), fulfilling the requirement for notification.
References

1. Guardrails for Amazon Bedrock: The official AWS documentation states, "With Guardrails for Amazon Bedrock, you can... configure a set of policies to safeguard your generative AI applications... You can create multiple guardrails, each with a different combination of policies. The policies in a guardrail include... Content filters to filter harmful content... [and] Denied topics to avoid unwanted topics."

Source: AWS Documentation, "Guardrails for Amazon Bedrock," Introduction.

2. Monitoring Guardrails with CloudWatch: The documentation further explains the notification mechanism: "Amazon Bedrock integrates with Amazon CloudWatch Events to notify you of interventions by a guardrail... You can create rules in CloudWatch Events that trigger programmatic actions in response to an event."

Source: AWS Documentation, "Monitor Guardrails for Amazon Bedrock."

3. Amazon Macie Functionality: "Amazon Macie is a data security service that discovers sensitive data by using machine learning and pattern matching... Macie automatically detects a large and growing list of sensitive data types, including personally identifiable information (PII)... in your Amazon S3 buckets."

Source: AWS Documentation, "What is Amazon Macie?"

4. AWS CloudTrail Functionality: "AWS CloudTrail is an AWS service that helps you enable operational and risk auditing, governance, and compliance of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail."

Source: AWS Documentation, "What Is AWS CloudTrail?"

Question 3

An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question. Which solution meets these requirements with the LEAST implementation effort?
Options
A: Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
B: Add a role description to the prompt context that instructs the model of the age range that the response should target.
C: Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
D: Summarize the response text depending on the age of the user so that younger users receive shorter responses.
Show Answer
Correct Answer:
Add a role description to the prompt context that instructs the model of the age range that the response should target.
Explanation
This solution uses prompt engineering, a technique that guides the model's output by providing specific instructions within the prompt itself. Adding a role description (e.g., "Explain this to a 10-year-old") leverages the model's in-context learning capabilities to adopt the appropriate tone, vocabulary, and style for the specified age range. This method is highly effective and requires only a minor modification to the input text, making it the solution with the least implementation effort compared to model retraining or multi-step processing.
References

1. Vendor Documentation: Google Cloud. (2024). Introduction to prompt design. Vertex AI Documentation. In the section "Prompt types," the "Persona prompt" is described as a way to assign a role to the model (e.g., "You are an expert in...") to tailor its response style, which aligns directly with the proposed solution.

2. Academic Publication: Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems 35. This paper defines Chain-of-Thought (CoT) as a method for solving arithmetic, commonsense, and symbolic reasoning problems (Section 2), distinguishing its purpose from stylistic control.

3. University Courseware: Jurafsky, D., & Manning, C. (2023). Lecture 10: Prompting, Instruction-Tuning, and RLHF. Stanford University, CS224N: Natural Language Processing with Deep Learning. The lecture discusses prompting as a low-effort way to guide model behavior without updating model weights, contrasting it with the higher effort of fine-tuning (instruction-tuning). Assigning a persona is a fundamental prompting technique.

Question 4

A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals. Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?
Options
A: User-generated content
B: Moderation logs
C: Content moderation guidelines
D: Benchmark datasets
Show Answer
Correct Answer:
Benchmark datasets
Explanation
Benchmark datasets are specifically created and curated by the research community for evaluating model performance on specific tasks, including fairness and bias detection. These datasets are pre-labeled, cleaned, and structured, allowing for standardized and repeatable evaluations. Using an existing, relevant benchmark dataset (e.g., Civil Comments, Jigsaw Toxicity Datasets) eliminates the need for data collection, annotation, and structuring, which are time-consuming and resource-intensive tasks. This makes it the most efficient option with the least administrative overhead for the company to systematically assess the LLM's outputs for bias.
References

1. Dinan, E., et al. (2020). Multi-dimensional Gender Bias Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). This paper introduces a benchmark dataset specifically for evaluating gender bias, illustrating the role of such datasets. (See Section 3: "A New Dataset for Gender Bias Classification", pp. 2-4). DOI: https://doi.org/10.18653/v1/2020.emnlp-main.391

2. Mehrabi, N., et al. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1-35. This survey discusses evaluation methodologies, highlighting the use of benchmark datasets as a primary tool for auditing and quantifying bias in models. (See Section 4: "BIAS MITIGATION"). DOI: https://doi.org/10.1145/3457607

3. Stanford University. (2023). CS224N: Natural Language Processing with Deep Learning. Course materials frequently emphasize the use of standardized benchmark datasets (e.g., GLUE, SQuAD) for model evaluation to ensure comparability and reproducibility, a principle that extends to fairness and bias evaluation. (See Lecture on "Model Evaluation").

Question 5

Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?
Options
A: Calculate the total cost of resources used by the model.
B: Measure the model's accuracy against a predefined benchmark dataset.
C: Count the number of layers in the neural network.
D: Assess the color accuracy of images processed by the model.
Show Answer
Correct Answer:
Measure the model's accuracy against a predefined benchmark dataset.
Explanation
The most direct and standard strategy for evaluating the accuracy of any classification model, including a foundation model (FM) applied to image classification, is to test it against a benchmark dataset. A benchmark dataset (e.g., ImageNet, CIFAR-10) contains a large set of images with pre-verified, correct labels, known as the "ground truth." The model's predictions on this dataset are compared to the ground truth labels to calculate performance metrics, with accuracy being the most common. This process quantifies how well the model generalizes to new, unseen data, which is the primary goal of model evaluation.
References

1. University Courseware:

Stanford University, CS231n: Convolutional Neural Networks for Visual Recognition, Module 1, "Setting up the data and the model". The course notes explain the necessity of a test set: "Finally, after the best hyperparameters are found, we evaluate the best model on the test set to get a measurement of how well the model is expected to perform on new data." This test set is a form of benchmark dataset.

2. Official Vendor Documentation:

Amazon Web Services (AWS), Amazon SageMaker Developer Guide, "Evaluate a Model". The documentation states: "After you have trained a model, you need to evaluate it to get an estimation of its quality on new data... by comparing the predictions that the model makes with the ground truth labels from a labeled test dataset." This directly supports using a benchmark dataset to measure accuracy.

3. Peer-reviewed Academic Publications:

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248-255. This paper introduced the ImageNet dataset, which became a fundamental benchmark for evaluating the performance of image classification models. The entire premise of the work is to provide a standardized, labeled dataset for robust model evaluation. (DOI: https://doi.org/10.1109/CVPR.2009.5206848)

Question 6

A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology. Which solution meets these requirements?
Options
A: Generative pre-trained transformers (GPT)
B: Residual neural network
C: Support vector machine
D: WaveNet
Show Answer
Correct Answer:
Generative pre-trained transformers (GPT)
Explanation
The core requirement is to translate natural language text into structured SQL queries. This is a natural language processing (NLP) task that involves both understanding the user's intent and generating syntactically correct code. Generative Pre-trained Transformers (GPT) are a class of large language models (LLMs) based on the transformer architecture. They excel at understanding context and generating coherent, structured text, including programming code. A GPT-based model can be trained or fine-tuned to specifically handle "text-to-SQL" tasks, providing an intuitive interface for non-technical users to query complex databases.
References

1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems 30 (NIPS 2017). Section 1, "Introduction," describes the transformer model's suitability for transduction tasks, which includes translation between languages (like English to SQL). Available from: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

2. Stanford University. (2023). CS224N: NLP with Deep Learning, Lecture 11: Transformers and Pretraining. This lecture material discusses how transformer-based models like GPT are pre-trained on vast text and code corpora, enabling them to perform tasks like code generation from natural language prompts.

3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. The abstract and introduction clearly state that ResNets were developed to address challenges in training very deep networks for image recognition. DOI: 10.1109/CVPR.2016.90

4. Stanford University. (2022). CS229: Machine Learning, Course Notes: Support Vector Machines. Section 1, "Margins, Intuition," describes SVMs as a method for finding an optimal separating hyperplane for classification tasks.

5. van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio. The abstract explicitly states, "This paper introduces WaveNet, a deep neural network for generating raw audio waveforms." Available from: https://arxiv.org/pdf/1609.03499.pdf

Question 7

Which metric measures the runtime efficiency of operating AI models?
Options
A: Customer satisfaction score (CSAT)
B: Training time for each epoch
C: Average response time
D: Number of training instances
Show Answer
Correct Answer:
Average response time
Explanation
Runtime efficiency for an operating AI model refers to its performance during the inference phase, which is when the model is actively making predictions on new data. The "average response time," also known as latency, is a direct measure of this efficiency. It quantifies the time elapsed between receiving a request and returning a prediction. A lower average response time indicates higher runtime efficiency, which is critical for real-time applications and a positive user experience. This metric specifically evaluates the model's speed in its deployed, operational state.
References

1. Google Cloud AI Platform Documentation: In the documentation for Model Monitoring, "latency" (response time) is listed as a primary performance metric for prediction nodes. It is defined as the "distribution of the amount of time, in seconds, that it takes for AI Platform Prediction to return a prediction."

Source: Google Cloud. "Understanding model monitoring." Vertex AI Documentation. Accessed October 2023. (Specifically, see the table of metrics under the "Drift detection" or "Performance monitoring" sections).

2. AWS SageMaker Documentation: The official documentation for monitoring SageMaker endpoints lists ModelLatency as a key invocation metric. This metric is defined as "the time elapsed, in microseconds, from when a request enters the container until the container is ready to return a response." This directly corresponds to response time.

Source: Amazon Web Services. "Monitor Amazon SageMaker with Amazon CloudWatch." Amazon SageMaker Developer Guide, section on "SageMaker Endpoint Invocation Metrics."

3. University Courseware (Stanford): In Stanford's course on Machine Learning Systems Design (CS 329S), lecture materials on "Model Serving" emphasize latency (response time) and throughput as the two main performance metrics for a deployed model. Latency is critical for user-facing applications.

Source: Chip Huyen. "CS 329S: Machine Learning Systems Design, Lecture 7: Model Serving." Stanford University, Winter 2021, Slides 11-14.

Question 8

Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
Options
A: Helps decrease the model's complexity
B: Improves model performance over time
C: Decreases the training time requirement
D: Optimizes model inference time
Show Answer
Correct Answer:
Improves model performance over time
Explanation
Ongoing pre-training, also known as continued pre-training or domain-adaptive pre-training, involves further training a general foundation model on a large corpus of domain-specific, unlabeled data. This process adapts the model's internal representations, vocabulary, and understanding to the nuances of the target domain (e.g., legal, medical, or financial text). By aligning the model with the specific data distribution of the target domain before task-specific fine-tuning, it achieves a better starting point, which consistently leads to improved performance and accuracy on downstream tasks within that domain.
References

1. Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8342–8356). The paper's central thesis, summarized in the abstract and demonstrated in Section 4 ("Results"), is that "pretraining on data from the target domain (domain-adaptive pretraining) leads to performance gains." (DOI: https://doi.org/10.18653/v1/2020.acl-main.740)

2. Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford University Center for Research on Foundation Models (CRFM). In Section 4.2.1 ("Adaptation"), the report discusses methods for adapting FMs, stating, "The goal of adaptation is to steer the behavior of a foundation model to better perform a desired downstream task." Continued pre-training is a key method to achieve this performance improvement. (Page 63).

3. Stanford University. (2023). CS224N: NLP with Deep Learning, Winter 2023 Lecture 12: Pretraining and Transfer Learning. The lecture notes explain that continued pretraining on a domain-specific corpus before fine-tuning helps the model learn the specific statistics and vocabulary of that domain, which improves final task performance.

Question 9

An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images. Which type of FM should the AI practitioner use to power the search application?
Options
A: Multi-modal embedding model
B: Text embedding model
C: Multi-modal generation model
D: Image generation model
Show Answer
Correct Answer:
Multi-modal embedding model
Explanation
A search application that handles both text and image queries requires a model capable of understanding and processing both data types (modalities) simultaneously. A multi-modal embedding model is specifically designed for this purpose. It converts the combined text and image input into a single, dense vector representation, known as an embedding, within a shared semantic space. This embedding can then be used to efficiently find and rank relevant items in a database by comparing vector similarity, which is the core mechanism of modern semantic search applications.
References

1. Official Vendor Documentation: Amazon Web Services (AWS) documentation for "Amazon Titan Multimodal Embeddings" explicitly states its primary use case: "By converting images and short text into numerical representations (known as embeddings), the model supports a wide variety of multimodal search, recommendation, and ranking tasks." This directly aligns with the question's scenario. (Source: AWS Documentation, Amazon Bedrock, "Amazon Titan models").

2. Peer-Reviewed Academic Publication: The foundational paper on CLIP, a model that creates a joint embedding space for images and text, describes its utility for retrieval tasks. The model learns a "multi-modal embedding space" to perform tasks like zero-shot image retrieval from text queries. (Source: Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8748-8763. Section 3.1.3).

3. University Courseware: Stanford University's course CS231n discusses models that create joint embeddings for vision and language. These models are designed to map images and text to a shared vector space, enabling tasks like retrieving images based on text descriptions (and vice-versa), which is a form of multi-modal search. (Source: Stanford University, CS231n: Convolutional Neural Networks for Visual Recognition, Spring 2023, Lecture 11: "Vision + Language").

Question 10

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?
Options
A: Purchase Provisioned Throughput for the custom model.
B: Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C: Register the model with the Amazon SageMaker Model Registry.
D: Grant access to the custom model in Amazon Bedrock.
Show Answer
Correct Answer:
Purchase Provisioned Throughput for the custom model.
Explanation
To use a custom model (a model that has been fine-tuned) for inference within the Amazon Bedrock service, it is mandatory to purchase Provisioned Throughput. This action allocates dedicated, managed inference capacity for the custom model, ensuring consistent throughput and performance. Once Provisioned Throughput is purchased, the custom model becomes available for real-time inference calls via the Amazon Bedrock API, using the specific Amazon Resource Name (ARN) of the provisioned capacity.
References

1. Amazon Bedrock User Guide, "Provisioned Throughput": The documentation explicitly states, "To use your custom models for inference, you must purchase Provisioned Throughput for them. You can't use custom models with the On-Demand throughput mode." This confirms that purchasing Provisioned Throughput is a required action. (Source: AWS Official Documentation).

2. Amazon Bedrock User Guide, "Custom models": The section on using custom models details the workflow, which involves fine-tuning or importing a model, followed by purchasing Provisioned Throughput to make it available for inference. The guide does not mention deploying to a SageMaker endpoint as a step for using the model within Bedrock. (Source: AWS Official Documentation).

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