10 free AWS AIF-C01 practice questions with the correct answer and a full explanation for each, taken from the CertStash pack of 452 questions. Work through them, then open each answer to check your reasoning.
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Question 1
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
Show answer and explanation
Correct answer: B. Partial dependence plots (PDPs)
Partial dependence plots (PDPs) are a key explainability tool that show how predicted values change as a feature varies, holding other features constant. This directly demonstrates the relationship between input features and model predictions, providing transparency to stakeholders about how the model makes forecasting decisions. PDPs help non-technical audiences understand model behavior without requiring knowledge of implementation details.
Why the other options are wrong
- A. Model training code is implementation detail, not explanation of model behavior to stakeholders.
- C. Sample training data does not explain how the model makes predictions or what it learned.
- D. Convergence tables show training progress metrics but do not explain model explainability or feature relationships.
Question 2
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Show answer and explanation
Correct answer: C. Develop a summarization chatbot.
A summarization chatbot using LLMs is the appropriate solution for extracting key points from legal documents. LLMs excel at understanding document content and generating concise summaries of important information. This approach leverages the natural language understanding capabilities of LLMs to identify and extract relevant key points from complex legal texts.
Why the other options are wrong
- A. Named entity recognition focuses on identifying specific entity types (names, organizations) rather than extracting broader key points from documents.
- B. A recommendation engine suggests items to users based on preferences, not extracting information from documents.
- D. Translation systems convert text between languages but do not extract or summarize key points.
Question 3
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Show answer and explanation
Question 4
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
Show answer and explanation
Correct answer: B. Accuracy
Accuracy is the standard metric for classification tasks, measuring the proportion of correct predictions out of total predictions. For an image classification model predicting plant diseases, accuracy directly answers the question of how many images were classified correctly, making it the appropriate evaluation metric.
Why the other options are wrong
- A. R-squared is used for regression tasks to measure variance explained, not for classification accuracy.
- C. Root mean squared error (RMSE) measures the magnitude of prediction errors in regression problems, not classification correctness.
- D. Learning rate is a hyperparameter controlling training step size, not an evaluation metric for model performance.
Question 5
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?
Show answer and explanation
Correct answer: A. Adjust the prompt.
Adjusting the prompt is the most direct and effective way to control LLM output characteristics including length and language. By explicitly specifying in the prompt that responses should be short and written in a specific language, the model will align its outputs with these requirements without requiring model changes or parameter adjustments.
Why the other options are wrong
- B. Changing model size may affect performance but does not directly control output length or language specificity.
- C. Increasing temperature makes outputs more random and diverse, not shorter or more aligned with specific language requirements.
- D. Increasing Top K value affects output diversity and randomness, not output length or language specification.
Question 6
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Show answer and explanation
Correct answer: C. Asynchronous inference
Asynchronous inference is designed for scenarios with large input sizes (up to 1 GB) and long processing times (up to 1 hour). It queues requests and processes them without requiring an immediate response, enabling the system to handle resource-intensive workloads while still providing results as near real-time as possible given the constraints. This is more cost-effective than real-time inference for these specifications.
Why the other options are wrong
- A. Real-time inference requires low latency and is not designed for 1-hour processing times or 1 GB input sizes.
- B. Serverless inference has constraints on payload size and processing duration incompatible with these requirements.
- D. Batch transform is for offline processing of large datasets, not for near real-time latency requirements.
Question 7
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?
Show answer and explanation
Correct answer: B. Use transfer learning.
Transfer learning is the strategy of adapting pre-trained models to new, related tasks without building models from scratch. This approach leverages knowledge learned on large datasets and applies it to domain-specific tasks, reducing training time and data requirements while maintaining performance. It is the standard technique for adapting pr-rained models to new applications.
Why the other options are wrong
- A. Increasing epochs affects training duration but is not a strategy for adapting pr-rained models to new tasks.
- C. Decreasing epochs would reduce training time but does not address adapting pr-rained models to new tasks.
- D. Unsupervised learning does not specifically address adapting pre-trained models to new related tasks.
Question 8
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?
Show answer and explanation
Correct answer: A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
SageMaker Ground Truth Plus Amazon SageMaker Ground Truth Plus provides human-in-the-loop validation where human annotators review and validate model outputs or annotations. This directly addresses the requirement to minimize the risk of incorrect annotations by ensuring human experts verify annotations for protective eyewear images, maintaining high accuracy through manual quality control.
Why the other options are wrong
- B. Data augmentation using a knowledge base increases data volume but does not minimize annotation errors or ensure accuracy.
- C. Amazon Rekognition performs image recognition but does not validate annotations or provide human review for accuracy assurance.
- D. Amazon QuickSight Q summarizes data for business intelligence but does not validate or ensure annotation accuracy.
Question 9
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
Show answer and explanation
Correct answer: A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
When Amazon Bedrock attempts to access S3 data encrypted with SSE-S3, the IAM role assumed by Bedrock must have the appropriate permissions to decrypt and read the objects. SSE-S3 encryption is managed by AWS, and the role needs s3:GetObject and related permissions to access the encrypted data. Without proper IAM permissions on the role, the access will fail regardless of the encryption key itself.
Why the other options are wrong
- B. Public access to S3 buckets would expose sensitive data and violates security best practices; it is not the solution to permission issues.
- C. Prompt engineering cannot solve access control failures; the underlying IAM permissions must be correct.
- D. The presence or absence of sensitive information in the data does not determine whether the FM can access it; permissions must still be granted.
Question 10
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?
Show answer and explanation
Correct answer: A. Deploy optimized small language models (SLMs) on edge devices.
To achieve the lowest latency for inference on edge devices, deploying optimized small language models (SLMs) directly on the edge devices eliminates network round-trip time to centralized APIs. SLMs are designed for efficiency and can run locally with minimal computational overhead, whereas large models and centralized APIs introduce network latency and dependency on external services.
Why the other options are wrong
- B. Large language models require significantly more computational resources and memory, making them impractical for edge deployment and increasing latency.
- C. Asynchronous communication with centralized APIs introduces network latency, defeating the goal of lowest-latency inference.
- D. Centralized LLM APIs are even slower than centralized SLM APIs and introduce both network and processing latency.
That was 10 of 452.
The full AWS AIF-C01 pack has all 452 questions, each with the answer, the explanation and why the other options are wrong, plus a questions-only copy for timed runs. US$39, paid once, with free monthly updates and a pass-or-your-money-back guarantee.