AIF-C01 Practice Test 39 — 15 Questions

Question 1

A data scientist uses Amazon Bedrock. The model responses are too long. Which parameter should they adjust to limit the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) the model can generate in a single response. By reducing this value, the data scientist caps the output length, preventing overly long responses. Temperature and top_p affect randomness and diversity, not length, while stop sequences define when generation halts but do not enforce a hard token limit.

Answer

temperature

Answer

stop sequences

Answer

top_p

Question 2

A data scientist is fine-tuning a foundation model on a custom dataset using Amazon SageMaker. After training, the model shows high accuracy on training data but poor on validation. Which action should be taken?

Accepted Answer

Reduce training epochs or add regularization. The model is overfitting, as indicated by high training accuracy but poor validation performance. Reducing training epochs or adding regularization (e.g., L1/L2 weight decay) directly addresses overfitting by limiting the model's capacity to memorize noise. In Amazon SageMaker, this can be implemented via hyperparameter tuning or by modifying the training script to include regularization terms.

Answer

Add dropout layers

Answer

Increase learning rate

Answer

Use a different foundation model

Question 3

Which THREE considerations are essential when deploying a generative AI application in a regulated industry such as healthcare?

Accepted Answer

Full audit trail of model inputs and outputs for accountability.. Regulated industries like healthcare require a complete audit trail of model inputs and outputs to ensure accountability and traceability. This is essential for compliance with regulations such as HIPAA, which mandates logging of all access and processing of protected health information (PHI). Without a full audit trail, it is impossible to verify that the model's decisions are compliant or to investigate potential violations.

Answer

Lowest possible inference latency for real-time responses.

Answer

Maximum creative freedom for the model to generate diverse responses.

Question 4

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in company-specific data and that harmful content is filtered out. Which two services or features should they use? (Choose TWO.)

Accepted Answer

Amazon Kendra. Amazon Kendra is correct because it provides a managed search service that indexes company-specific data sources, enabling the Bedrock chatbot to retrieve relevant documents and ground its responses in authoritative information. Bedrock Guardrails is correct because it allows you to define content filters and topic policies to block harmful or undesirable outputs, ensuring the chatbot adheres to safety and compliance requirements.

Answer

Bedrock Agents

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Amazon Comprehend

Answer

Amazon SageMaker JumpStart

Question 5

A data science team is using Amazon SageMaker to train multiple models with different hyperparameters. They want to track metrics, compare runs, and reproduce the best result. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct feature because it is specifically designed to track, organize, and compare machine learning training runs (trials) with different hyperparameters and metrics. It allows data scientists to log parameters, metrics, and artifacts for each run, compare results across runs, and retrieve the exact configuration needed to reproduce the best-performing model.

Answer

SageMaker Model Registry

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SageMaker Debugger

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SageMaker Autopilot

Question 6

A company wants to build an ML model to predict customer lifetime value. The dataset includes numerical features (age, income) and categorical features (gender, region). Which TWO preprocessing steps should be applied to the categorical features before training a linear regression model? (Choose TWO.)

Accepted Answer

Imputation of missing values. Imputation of missing values is necessary for categorical features because linear regression cannot handle missing data. Common practice is to impute with the mode. One-hot encoding converts categorical variables into binary vectors, avoiding ordinal bias that label encoding would introduce. These two steps ensure the categorical data is suitable for linear regression.

Answer

Feature scaling

Answer

Principal component analysis (PCA)

Answer

Label encoding

Question 7

A data scientist wants to deploy a custom model built with TensorFlow to Amazon SageMaker for real-time inference. Which TWO steps are required? (Choose two.)

Accepted Answer

Upload the model artifacts to an S3 bucket. SageMaker requires model artifacts (the trained model files) to be stored in an S3 bucket before they can be used for inference. When deploying a custom TensorFlow model, you must upload the saved model (e.g., in SavedModel format) to S3, and then SageMaker will download it to the inference container during endpoint creation.

Answer

Create an Amazon ECR repository for the inference container

Answer

Submit a training job to SageMaker

Answer

Convert the model to ONNX format

Question 8

A company wants to build a generative AI application that generates personalized marketing emails based on customer data. They have a small dataset of past emails. Which AWS service should they use to fine-tune a foundation model with their data?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is correct because it provides a fully managed environment for fine-tuning foundation models using custom datasets, such as the company's small dataset of past emails. With SageMaker's built-in JumpStart capabilities, you can access and fine-tune pre-trained models (e.g., from Hugging Face or AWS) using your own data, enabling personalized marketing email generation without managing underlying infrastructure.

Answer

Amazon Comprehend

Answer

AWS Lambda

Answer

Amazon Bedrock

Question 9

A company wants to track API calls made to Amazon SageMaker for audit purposes. Which AWS service should they enable?

Accepted Answer

AWS CloudTrail. AWS CloudTrail is the correct service because it records API activity across AWS services, including Amazon SageMaker. By enabling CloudTrail, the company can capture all SageMaker API calls (e.g., CreateModel, InvokeEndpoint) for audit, compliance, and security analysis. CloudTrail logs provide details such as the identity of the caller, the time of the call, and the request parameters, which are essential for auditing.

Answer

Amazon Macie

Answer

AWS Config

Answer

Amazon CloudWatch Logs

Question 10

A company wants to use Amazon Bedrock to generate personalized marketing emails. They have thousands of customer profiles with demographic data. To generate tailored content efficiently, the application must dynamically insert customer-specific information into prompts. Which prompt management technique is BEST suited for this?

Accepted Answer

Prompt flows with variables. Prompt flows with variables allow the application to dynamically insert customer-specific data (e.g., name, age, purchase history) into a base prompt template at runtime. This technique avoids retraining or switching models for each customer, enabling efficient, personalized content generation without modifying the underlying foundation model.

Answer

Using a larger foundation model to understand the entire customer base

Answer

Fine-tuning the model on all customer profiles

Answer

Prompt versioning

Question 11

A company uses Amazon SageMaker to train sensitive ML models. Which AWS service should they use to encrypt the training data and model artifacts at rest?

Accepted Answer

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.

Answer

AWS Secrets Manager

Answer

AWS CloudHSM

Answer

AWS Certificate Manager

Question 12

A large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?

Accepted Answer

Remove zip code and any income-correlated features from the training data. The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.

Answer

Randomly show luxury recommendations to a subset of users regardless of their behavior

Answer

Add more training data from less affluent areas to balance the dataset

Answer

Implement a separate recommendation pipeline for luxury items based only on search history

Question 13

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. Training jobs are frequently failing with 'OutOfMemoryError'. The training algorithm uses PyTorch. How should the data scientist solve this without reducing model accuracy?

Accepted Answer

Use SageMaker Pipe mode for data ingestion. SageMaker Pipe mode streams training data directly from S3 into the algorithm without first downloading it to the local disk, which drastically reduces memory consumption. This allows the model to handle large datasets that would otherwise cause an OutOfMemoryError when using the default File mode, all while preserving the original model architecture and accuracy.

Answer

Reduce the number of layers in the model

Answer

Increase the batch size

Answer

Use a smaller instance type with less memory

Question 14

Refer to the exhibit. A data scientist created this endpoint config for a foundation model in Amazon SageMaker. However, the endpoint fails to scale under load. What is the most likely reason?

Accepted Answer

Missing AutoScaling configuration. The endpoint fails to scale under load because the endpoint configuration shown lacks an AutoScaling policy. Without AutoScaling, SageMaker will not automatically adjust the number of instances based on traffic, so even if the initial instance count is 1, the endpoint cannot add more instances to handle increased load. AutoScaling must be explicitly configured via Application Auto Scaling to define scaling policies and target tracking metrics.

Answer

Variant weight is 1.0

Answer

Instance type is too small

Answer

InitialInstanceCount is 1

Question 15

A machine learning team is building a credit risk model and discovers that the training data has a significant imbalance in loan approval rates between two demographic groups. They decide to reweight the training samples using a preprocessing technique. Which SageMaker Clarify feature can help compute the appropriate sample weights to achieve demographic parity?

Accepted Answer

Clarify preprocessing (reweighting). SageMaker Clarify's preprocessing (reweighting) feature directly computes sample weights to adjust for imbalances in training data, enabling demographic parity by assigning higher weights to underrepresented groups. This is a pre-training bias mitigation technique that modifies the dataset before model training, aligning with the team's goal of reweighting samples to address loan approval rate disparities.

Answer

Clarify post-training bias metrics

Answer

Model Monitor bias drift

Answer

Clarify explainability (SHAP)