AIF-C01 Flashcards — Free AWS Certified AI Practitioner AIF-C01 Study Cards

Disable model training data logging in the AWS console.

Disabling model training data logging in the AWS console prevents Amazon Bedrock from using customer inference data to improve the underlying foundation model. This setting ensures compliance with the requirement that no patient data is used for model training, as Bedrock offers a specific toggle to opt out of data sharing for model improvement.

Implement Retrieval-Augmented Generation (RAG) with a product knowledge base.

Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base by retrieving relevant product documents before generating text. This directly addresses factual inaccuracies because the model references authoritative data rather than relying solely on its parametric memory, which may contain outdated or incorrect information.

Use a RAG architecture with the company documentation as the knowledge base.

Retrieval-Augmented Generation (RAG) architecture retrieves relevant chunks from the company documentation at query time and injects them into the prompt, ensuring the model's response is grounded solely in the provided documents. This approach prevents the model from relying on its internal training data or generating information outside the documentation, which is critical for a closed-domain chatbot.

Set temperature to 0 and top_p to 1.

Setting temperature to 0 and top_p to 1 forces the model to always select the highest-probability token at each step, producing deterministic and repeatable outputs. This is essential for auditing and compliance in financial services, where consistency is required. Any nonzero temperature introduces randomness, which undermines determinism.

Amazon ElastiCache

Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

AWS Key Management Service (KMS)

AWS Key Management Service (KMS) allows customers to create and manage encryption keys used to encrypt data at rest in SageMaker, including training data and model artifacts.

Configure the S3 bucket policy to allow access from the Bedrock service role and add a Bedrock resource policy allowing the bucket

Cross-account access requires both the S3 bucket policy to grant the Bedrock service role and the Bedrock resource policy to allow the bucket, ensuring least privilege.

Amazon SageMaker Autopilot

Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

Amazon S3

Amazon S3 is the correct choice because it is designed for cost-effective, scalable storage of unstructured data (images, videos) and integrates natively with Amazon SageMaker for low-latency data retrieval during training jobs. S3 provides high throughput and can be accessed directly from SageMaker training instances without the need for file system mounting, making it ideal for large-scale ML workloads.

Amazon SageMaker

Amazon SageMaker is the correct choice because it provides a fully managed environment for building, training, and deploying machine learning models at scale. For real-time anomaly detection on streaming IoT data, SageMaker can host a trained model as a real-time endpoint that processes incoming sensor readings via Amazon Kinesis Data Streams or AWS Lambda, minimizing operational overhead by handling infrastructure, scaling, and monitoring automatically.

Include few-shot examples in the system prompt to demonstrate the desired tone.

Few-shot prompting allows you to provide the model with examples of desired responses directly in the system prompt, guiding the model's tone and style without modifying its underlying weights. This technique is ideal for brand voice consistency when fine-tuning is not an option, as it leverages in-context learning to influence output behavior.

Amazon SageMaker Training job.

Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

Refine the prompt with more descriptive language and adjust the CFG scale and inference steps.

Refining the prompt with more descriptive language helps the model better interpret the user's intent, while adjusting the CFG (Classifier-Free Guidance) scale controls how strictly the model adheres to the prompt, and increasing inference steps allows the diffusion process to produce higher-quality, artifact-free images. These are standard hyperparameters in diffusion-based image generation models on Amazon Bedrock, directly addressing both artifacts and prompt mismatch.

Amazon Bedrock Guardrails.

Amazon Bedrock Guardrails is the correct feature because it is specifically designed to enforce content policies, filter toxic or harmful content, and block undesirable topics in generative AI responses. It provides configurable thresholds for hate, insults, sexual content, violence, and other harmful categories, ensuring compliance with safety requirements without modifying the underlying model.

Regularly audit the model for demographic bias

Regularly auditing the model for demographic bias is a core responsible AI practice, especially for identity verification systems where biased outcomes could lead to unfair treatment of certain customer groups. Amazon Rekognition's facial analysis and comparison features must be tested across diverse demographics to ensure equitable performance, as bias can arise from imbalanced training data or algorithmic artifacts.

Use Amazon SageMaker Clarify to detect bias in predictions

Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.