AIF-C01 Practice Test 32 — 15 Questions

Question 1

A company is developing a speech-to-text application for a diverse user base. To ensure inclusive design, they test the model with different accents and dialects. They find that error rates are higher for certain accents. Which responsible AI principle is most directly violated?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Fairness. Fairness requires that AI systems do not create or reinforce disparate outcomes for different groups. Higher error rates for certain accents indicate a fairness issue.

Answer

Robustness

Answer

Veracity

Answer

Privacy and security

Question 2

A company uses a Bedrock Agent to handle customer support tickets. The agent needs to look up order status from a legacy API that requires authentication. The agent should also escalate to a human if the query is not supported. Which combination of components should the developer configure?

Accepted Answer

Define an action group with a Lambda function that calls the legacy API, and configure the agent instructions to escalate when it cannot answer.. Action groups define APIs or tools the agent can call, with Lambda functions implementing the logic (including authentication). The agent can decide to hand off to a human based on its instructions. Knowledge Bases are not needed for API lookup, and Guardrails do not handle escalations.

Answer

Configure a Guardrail to block unsupported queries and return a static response.

Answer

Use a Bedrock Knowledge Base to store order status information and rely on RAG to answer.

Answer

Fine-tune the model on historical tickets so it can guess order status.

Question 3

Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker. The job failed with the error shown. What is the most likely cause?

Accepted Answer

The batch size is too large for the instance's GPU memory. The error message indicates a CUDA out-of-memory error, which occurs when the GPU memory is insufficient for the requested batch size. Option D is correct because increasing the batch size beyond the GPU's memory capacity causes the training job to fail with this specific error.

Answer

The S3 input path is incorrect

Answer

The IAM role does not have permission to access S3

Answer

The training code has a syntax error

Question 4

A team is training a binary classification model using Amazon SageMaker. They notice that the training accuracy is 99% but the test accuracy is only 70%. Which technique should they apply first to address this?

Accepted Answer

Apply regularization. The high training accuracy (99%) paired with significantly lower test accuracy (70%) is a classic symptom of overfitting, where the model memorizes the training data instead of learning generalizable patterns. Regularization (Option B) is the first-line technique to combat overfitting by adding a penalty to the loss function (e.g., L1 or L2 regularization), which discourages overly complex decision boundaries. In Amazon SageMaker, this can be implemented via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in a custom framework.

Answer

Reduce training data

Answer

Increase learning rate

Answer

Increase model complexity

Question 5

A data scientist needs to restrict access to a SageMaker notebook instance to only the corporate network. Which configuration should they use?

Accepted Answer

Place the notebook instance in a VPC and configure security groups to allow only corporate IP ranges. VPC isolation places the notebook instance inside a VPC, and security groups can restrict inbound traffic to the corporate network IP range.

Answer

Enable multi-factor authentication for the notebook

Answer

Use an IAM policy to allow only corporate users

Answer

Use a SageMaker lifecycle configuration to block external IPs

Question 6

A company wants to use AWS Lake Formation to govern access to data used for AI training. They need to ensure that only approved columns of sensitive tables are visible to data scientists. Which THREE steps should they implement? (Choose THREE)

Accepted Answer

Use AWS Glue crawlers to catalog the data and populate the Data Catalog. Lake Formation allows you to register S3 locations, create resource links, and grant permissions at column level. The other options are not specific to Lake Formation column-level access control.

Answer

Create a SageMaker notebook instance and attach an IAM role

Answer

Enable S3 versioning on the training data bucket

Question 7

An organization wants to control which topics their AI chatbot can discuss. For example, they want to block all conversations about investment advice. Which Amazon Bedrock Guardrails feature should they configure?

Accepted Answer

Topic restrictions. Topic restrictions allow you to define denied topics. When the model's input or output matches a denied topic, the guardrail blocks the interaction.

Answer

Contextual grounding check

Answer

Content filtering with category-based harmful content filters

Answer

Sensitive information filters

Question 8

Which TWO AWS services can be used to build a chatbot that responds to customer inquiries using a company's documentation as source? (Select two.)

Accepted Answer

Amazon Bedrock with RAG. Amazon Bedrock with RAG (Retrieval-Augmented Generation) enables a chatbot to retrieve relevant chunks from a company's documentation and use them as context for a foundation model to generate accurate, grounded responses. This approach directly addresses the need to answer customer inquiries using proprietary or domain-specific source material.

Answer

Amazon Polly

Answer

Amazon Transcribe

Answer

Amazon Lex

Question 9

A team is deploying a regression model for loan approval. To ensure transparency for regulators, they need to explain individual predictions. Which interpretability method can provide local explanations by approximating the model with a simpler surrogate?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct choice because it generates local explanations by fitting a simpler, interpretable surrogate model (e.g., linear regression or decision tree) around a single prediction. This allows the team to explain why a specific loan application was approved or rejected, meeting regulatory transparency requirements without needing access to the original model's internals.

Answer

SHAP values

Answer

Partial dependence plots

Answer

Permutation feature importance

Question 10

A company uses Amazon Bedrock and wants to ensure that the model outputs are grounded in a set of provided documents to reduce hallucinations. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Enable the grounding check in Bedrock Guardrails. Bedrock Guardrails' grounding check verifies that model responses are supported by source documents. Knowledge Bases for Amazon Bedrock provide a RAG architecture to retrieve relevant document chunks.

Answer

Configure a word filter to block ungrounded phrases

Answer

Enable model invocation logging to S3

Answer

Fine-tune the model on the documents

Question 11

Which TWO factors should be considered when choosing between a CPU-based instance and a GPU-based instance for training a machine learning model on Amazon SageMaker? (Choose two.)

Accepted Answer

The size of the dataset. The size of the dataset directly impacts whether a GPU's parallel processing capabilities are beneficial. GPU instances excel at performing many matrix operations simultaneously, which is critical for large datasets where mini-batch gradient descent can be parallelized. For smaller datasets, the overhead of transferring data to GPU memory may negate the performance gains, making CPU instances more cost-effective.

Answer

The number of layers in the model

Answer

The AWS Region

Answer

The choice of hyperparameter optimizer

Question 12

Refer to the exhibit. An IAM policy is attached to a user. Which models can the user invoke?

Accepted Answer

Only Claude v2. The IAM policy explicitly allows the `bedrock:InvokeModel` action only on the resource ARN `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`. This means the user can invoke only the Claude v2 model. No other models, including other Claude versions or any model with 'claude' in its name, are permitted because the resource ARN is exact and does not use wildcards.

Answer

No models

Answer

Claude v2 and any model with a name containing 'claude'

Answer

Any model in the account

Question 13

Which of the following is a key advantage of using a pre-trained foundation model over training a model from scratch?

Accepted Answer

Reduces the amount of labeled data and compute resources needed. Pre-trained foundation models, such as those based on transformer architectures, have already learned general language patterns from vast, diverse datasets during their initial training. This transfer learning approach drastically reduces the need for large amounts of labeled data and extensive compute resources when adapting the model to a specific downstream task, as only a relatively small fine-tuning step is required.

Answer

Eliminates the need for any fine-tuning

Answer

Guarantees perfect accuracy on domain-specific tasks

Answer

Allows the model to work offline without any cloud infrastructure

Question 14

A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can analyze text for sentiment, key phrases, and — critically — toxicity and inappropriate content using built-in or custom classifiers. This directly matches the requirement to moderate user-generated text with high accuracy, as it can detect hate speech, profanity, and other harmful language.

Answer

Amazon Translate

Answer

Amazon Rekognition

Answer

Amazon Polly

Question 15

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).

Answer

Label encoding

Answer

Normalization

Answer

One-hot encoding