AIF-C01 Practice Test 24 — 15 Questions

Question 1

A financial services company is using Amazon Bedrock to generate investment summaries. They want to ensure that the model outputs are factually accurate and based on the latest market data. Which combination of services should they use to achieve this? (Select TWO)

Accepted Answer

Amazon Aurora with the pgvector extension. Amazon Aurora with the pgvector extension (Option D) enables storing and querying vector embeddings directly within a PostgreSQL-compatible database, which is essential for Retrieval-Augmented Generation (RAG). When combined with Amazon Bedrock Knowledge Bases (Option E), it allows the company to retrieve the most current market data as vector embeddings, ensuring the generated investment summaries are grounded in factual, up-to-date information rather than relying solely on the model's static training data.

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Amazon SageMaker Ground Truth for data labeling

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Amazon DynamoDB as the knowledge base store

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Amazon Kendra for indexing the knowledge base

Question 2

Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)

Accepted Answer

AWS Glue. AWS Glue is a fully managed ETL service that can be used to preprocess data for machine learning by cleaning, transforming, and enriching raw data before feeding it into ML models. It provides built-in transforms and can handle both structured and semi-structured data, making it suitable for preparing large datasets for training.

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Amazon Athena

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Amazon Redshift

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AWS Lambda

Question 3

A company is using Amazon Bedrock to generate product descriptions. They notice that the model sometimes produces descriptions that contain factual errors about the products. Which TWO actions should they take to improve factual accuracy?

Accepted Answer

Implement Retrieval Augmented Generation (RAG) with a product knowledge base. Option A is correct because Retrieval Augmented Generation (RAG) grounds the model's output in a curated product knowledge base, allowing it to retrieve and cite authoritative facts during generation. This directly reduces hallucinations by ensuring the model references verified data rather than relying solely on its parametric memory.

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Reduce the temperature parameter to 0.1

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Increase the max_tokens to allow longer descriptions

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Use human reviewers to correct errors after generation

Question 4

A team trains a model using Amazon SageMaker built-in XGBoost. After training, they want to evaluate feature importance. Which SageMaker feature allows them to view this?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in monitoring and visualization capabilities, including the ability to capture feature importance metrics (e.g., gain, cover, weight) from XGBoost training jobs. It automatically saves these metrics to Amazon S3 and allows you to view them through the SageMaker Studio Debugger dashboard or by querying the saved tensors, enabling direct evaluation of feature importance without additional custom code.

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SageMaker Experiments

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SageMaker Autopilot

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SageMaker Model Monitor

Question 5

A data scientist is preparing data for a classification task. Which TWO techniques are commonly used for handling missing values? (Choose two.)

Accepted Answer

Imputing with mean. Imputing with the mean is a common technique for handling missing values in numerical features because it preserves the overall distribution of the data without reducing the dataset size. This method replaces each missing entry with the arithmetic mean of the non-missing values in that column, which is simple to implement and works well when data is missing completely at random (MCAR).

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Label encoding

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Normalization

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One-hot encoding

Question 6

A data scientist wants to perform automatic model tuning (hyperparameter optimization) on SageMaker. They need to find the best hyperparameters for a gradient boosting model. Which strategy is BEST for this task?

Accepted Answer

Bayesian optimization. Bayesian optimization is the best strategy for automatic model tuning on SageMaker because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next. This approach is far more sample-efficient than random or grid search, making it ideal for expensive-to-evaluate models like gradient boosting, where each training run consumes significant time and compute resources.

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Random search

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Grid search

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Exhaustive search

Question 7

A developer is using Amazon Bedrock to generate code snippets. The model often produces insecure code. Which prompt engineering technique is MOST effective to improve security?

Accepted Answer

Include specific instructions to avoid common security vulnerabilities. Option D is correct because directly instructing the model to avoid specific security vulnerabilities (e.g., SQL injection, buffer overflows) is the most explicit and effective way to constrain the output. Amazon Bedrock models respond well to clear, imperative instructions in the system prompt or user message, making this a direct application of prompt engineering for safety. Chain-of-thought or few-shot examples may improve reasoning or style but do not guarantee the model will avoid insecure patterns unless explicitly told to do so.

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Use chain-of-thought prompting to step through the code

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Provide few-shot examples of secure code

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Set max_tokens to a low value to limit output

Question 8

A company is using Amazon Bedrock to build a text-to-SQL application. They want to ensure that the generated SQL queries are valid and safe. Which approach is BEST?

Accepted Answer

Configure a guardrail to filter and validate the generated SQL. Amazon Bedrock guardrails provide a native, configurable mechanism to filter and validate model outputs, including SQL queries, against defined policies such as regex patterns, denied topics, and content filters. This approach directly addresses both validity and safety without requiring additional model training or external validation services, making it the most integrated and efficient solution for ensuring generated SQL is syntactically correct and free of harmful operations like DROP or DELETE.

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Fine-tune the model on a dataset of valid SQL queries

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Use a separate model to validate the SQL after generation

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Limit the max_tokens to 50 to reduce complexity

Question 9

Which TWO practices help ensure transparency in AI systems? (Choose 2)

Accepted Answer

Use model-agnostic explainability tools like SHAP. Option B is correct because model-agnostic explainability tools like SHAP (SHapley Additive exPlanations) provide post-hoc explanations for any machine learning model by computing feature contributions based on cooperative game theory. This allows stakeholders to understand how each input feature influences a prediction, directly supporting transparency without requiring access to the model's internal structure.

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Combine multiple models to obscure decision logic

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Remove all features except the most predictive ones

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Use black-box models to protect proprietary algorithms

Question 10

Which TWO of the following are key components of a responsible AI governance framework?

Accepted Answer

Develop and enforce AI ethics policies and standards. Option A is correct because a responsible AI governance framework must include the development and enforcement of AI ethics policies and standards to ensure alignment with societal values, fairness, and accountability. These policies guide the design, deployment, and monitoring of AI systems, embedding ethical principles such as transparency, privacy, and non-discrimination into the AI lifecycle. Without such policies, organizations risk deploying AI that violates ethical norms or regulatory expectations.

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Focus solely on compliance with legal regulations

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Minimize human involvement in AI lifecycle decisions

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Deploy AI models as black boxes to avoid scrutiny

Question 11

A data scientist is working with a dataset that contains both numerical and categorical features. Which algorithm is commonly used for regression tasks in AWS SageMaker?

Accepted Answer

Linear Learner. Linear Learner is the correct choice because it is a supervised learning algorithm in AWS SageMaker specifically designed for both regression and classification tasks. It can handle datasets with mixed numerical and categorical features (after appropriate encoding) and provides built-in mechanisms for training linear models, including automatic model tuning and distributed training.

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K-Means

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BlazingText

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Linear Learner

Question 12

A company wants to use a pre-trained generative AI model to analyze customer feedback. They need to adjust the model for their specific domain without retraining from scratch. Which approach is MOST suitable?

Accepted Answer

Fine-tuning the model on domain-specific data. Fine-tuning is the most suitable approach because it takes a pre-trained generative AI model and updates its weights using a smaller, domain-specific dataset (e.g., customer feedback transcripts). This allows the model to adapt to the company's specific terminology, sentiment patterns, and context without the massive computational cost and data requirements of training from scratch. It preserves the general language understanding from pre-training while specializing the model for the target domain.

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Reinforcement Learning from Human Feedback (RLHF)

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Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 13

A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practices should they follow to optimize the fine-tuning process?

Accepted Answer

Start with a base model that is already strong in the domain.. Starting with a base model that is already strong in the domain (Option A) is correct because it reduces the amount of fine-tuning data and compute required. Amazon Bedrock provides access to various foundation models (e.g., Anthropic Claude, Amazon Titan) that have been pre-trained on diverse corpora; selecting one that is already proficient in the target domain (e.g., legal or medical summarization) means the model's existing knowledge can be adapted with fewer training steps, leading to better performance and lower risk of catastrophic forgetting.

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Use the default hyperparameters without tuning.

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Train for as many epochs as possible.

Question 14

A company is using a foundation model on Amazon Bedrock to generate customer support responses. They notice that the model sometimes produces harmful or offensive content. Which approach is MOST effective to mitigate this issue?

Accepted Answer

Configure Amazon Bedrock Guardrails with content filters. Amazon Bedrock Guardrails provides configurable content filters that can block harmful, offensive, or inappropriate content in both user inputs and model outputs. This is the most effective approach because it operates at the inference layer, applying safety policies consistently across all requests without requiring model retraining or manual review. Prompt engineering alone is unreliable, and fine-tuning may not generalize to all harmful content patterns.

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Use prompt engineering to instruct the model to avoid harmful content

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Enable model invocation logging to review and block responses

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Fine-tune the model on a curated dataset of safe responses

Question 15

A company is building a generative AI application using Amazon Bedrock and needs to ensure that the model does not generate outputs containing personally identifiable information (PII). Which TWO actions should the company take? (Choose 2)

Accepted Answer

Implement a custom AWS Lambda function to scan and redact PII from inputs and outputs.. Option A is correct because a custom AWS Lambda function can be integrated into the application workflow to programmatically scan and redact PII from both inputs and outputs before they reach or leave the Bedrock model. This provides a flexible, code-driven approach to data sanitization, allowing the use of libraries like Amazon Comprehend or regex patterns to detect and mask PII entities such as names, addresses, and social security numbers.

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Use AWS Identity and Access Management (IAM) policies to restrict model access.

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Enable Amazon CloudWatch Logs to capture and audit model outputs.

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Place the Bedrock model endpoint within a private VPC.