AIF-C01 Practice Test 38 — 15 Questions

Question 1

A company is using Amazon Bedrock to generate code snippets. Developers report that the generated code sometimes contains security vulnerabilities. Which action should the team take to mitigate this risk?

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

Accepted Answer

Add a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities.. Adding a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities directly influences the model's output at inference time. Amazon Bedrock supports system prompts that act as high-level instructions to guide the foundation model's behavior, making this a proactive, scalable mitigation that does not require manual intervention or architectural changes.

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Deploy the model in a sandbox environment to limit its access to sensitive systems.

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Implement a manual code review process after generation.

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Reduce the temperature parameter to 0 to make the output deterministic.

Question 2

A data scientist wants to host a pre-trained model on Amazon SageMaker for real-time inference with minimal latency. Which approach should they use?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency, synchronous inference. They keep the model loaded and ready to respond to individual requests, making them ideal for real-time applications where minimal latency is critical.

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Run inference using AWS Lambda with the model packaged as a container

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Use SageMaker batch transform

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Create a SageMaker asynchronous inference endpoint

Question 3

A SageMaker endpoint is configured with automatic scaling. The model's inference time is 50ms, and traffic increases gradually. What scaling metric should be used to add instances before latency increases?

Accepted Answer

Invocations per instance. D is correct because 'Invocations per instance' is a custom metric that directly measures the number of inference requests each instance is handling. By setting a target value for this metric, the scaling policy can proactively add instances when the per-instance request count approaches a threshold, preventing latency increases before they occur. This is the recommended approach for SageMaker endpoints with gradual traffic increases, as it anticipates demand rather than reacting to latency spikes.

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Memory utilization

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Concurrent requests

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CPU utilization

Question 4

Which TWO of the following are types of feature scaling?

Accepted Answer

Standardization. Standardization (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.

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One-hot encoding

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Principal Component Analysis (PCA)

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Binning

Question 5

A company wants to use Amazon SageMaker Ground Truth to build a labeled dataset for a custom object detection model. Which TWO labeling strategies are available? (Choose two.)

Accepted Answer

Private workforce labeling (company employees). Amazon SageMaker Ground Truth supports private workforce labeling where company employees (e.g., via a corporate directory or invited users) perform manual annotation. This is ideal for sensitive data or domain-specific tasks like custom object detection, where internal expertise ensures high label accuracy.

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Automated labeling using pre-trained models

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Active learning with manual verification

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Fully automated labeling via AWS Lambda

Question 6

A research team is using Amazon SageMaker to fine-tune a large language model. They want to optimize training cost and time without sacrificing model quality. Which THREE strategies should they implement? (Choose 3)

Accepted Answer

Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA.. Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) freeze the pre-trained model weights and inject trainable rank decomposition matrices into specific layers. This drastically reduces the number of trainable parameters (often by 10,000x), lowering memory and compute requirements while preserving model quality, making it ideal for cost- and time-sensitive fine-tuning.

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Use a larger instance type with more GPUs.

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Increase the batch size to the maximum that fits in GPU memory.

Question 7

A company wants to classify customer emails into categories (e.g., complaint, inquiry, feedback) using a foundation model. Which approach is MOST efficient?

Accepted Answer

Use Amazon Comprehend for custom classification. Amazon Comprehend provides a managed custom classification API that is purpose-built for text classification tasks like categorizing emails. It requires only a small set of labeled data to train a custom classifier, eliminating the need to manage infrastructure or fine-tune large models, making it the most efficient choice for this specific use case.

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Train a custom model using Amazon SageMaker

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Fine-tune a large language model on labeled emails

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Use Amazon Lex with a classifier intent

Question 8

A social media company needs to automatically detect and flag toxic comments in multiple languages. They have a large stream of user comments and require real-time moderation. Which AWS 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 perform real-time toxicity detection across multiple languages using its built-in content moderation and custom classification capabilities. It analyzes text streams to identify toxic comments (e.g., hate speech, threats) and integrates with AWS streaming services like Amazon Kinesis for real-time processing.

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Amazon Lex

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Amazon Rekognition

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Amazon Translate

Question 9

A financial services company uses Amazon Bedrock to power a customer-facing chatbot that provides investment advice. The company must ensure that the chatbot's responses comply with regulatory standards, meaning that the model should not generate advice that is speculative or promises returns. The company has implemented Bedrock Guardrails with content filters. However, during testing, the chatbot still generates responses that violate the guidelines. A review of the guardrail configuration shows that the content filters are set to the lowest sensitivity. The company wants to enforce stricter filtering without completely blocking legitimate responses. What should the company do?

Accepted Answer

Increase the sensitivity of the content filters in the Bedrock Guardrails configuration.. Increasing the sensitivity of the content filters in Bedrock Guardrails directly addresses the issue: the current filters are set to the lowest sensitivity, allowing speculative or promise-based responses to pass through. By raising the sensitivity, the guardrails will block more non-compliant content while still permitting legitimate investment advice, striking the required balance between regulatory compliance and functionality.

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Use a different foundational model that has built-in compliance filters.

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Configure the chatbot to route all responses to a human reviewer before delivering to the customer.

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Add a deny topic for investment advice to completely block that topic.

Question 10

A company deploys a machine learning model for resume screening. They want to measure whether the model selects candidates proportionally across different demographic groups. Which fairness metric is most appropriate?

Accepted Answer

Disparate impact. Disparate impact measures the ratio of favorable outcomes between groups, commonly used to assess whether a selection process disproportionately affects a protected group.

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Demographic parity

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Accuracy parity

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Equalized odds

Question 11

An AI team uses the IAM policy shown in the exhibit to control endpoint creation. Why does this policy support responsible AI?

Accepted Answer

It ensures data capture is enabled for model monitoring. The IAM policy includes a condition that enforces the `DataCaptureConfig.EnableCapture` parameter to be set to `true` when creating a SageMaker endpoint. This ensures that model monitoring data is automatically collected, which is a key practice for responsible AI as it allows continuous monitoring of model performance, bias detection, and drift analysis. Without data capture, teams cannot audit or validate model behavior in production, undermining accountability and transparency.

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It requires human approval before deploying any model

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It prevents the use of GPU instances to reduce cost

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It restricts endpoints to only use models built in SageMaker

Question 12

A data scientist is building a binary classification model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraud). Which metric is most appropriate to evaluate model performance?

Accepted Answer

F1-score. In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.

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Accuracy

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Recall

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Precision

Question 13

A financial services company needs to deploy a real-time fraud detection model with sub-100ms inference latency. The model is a large ensemble requiring 8 GB of memory per request. The workload has bursty traffic. Which Amazon SageMaker deployment strategy best meets these requirements?

Accepted Answer

Use a single ml.r5.2xlarge instance with an auto-scaling policy based on CPU utilization. A single ml.r5.2xlarge instance provides 16 GB of memory, which can handle the 8 GB per request requirement, and SageMaker real-time endpoints with auto-scaling based on CPU utilization can dynamically adjust to bursty traffic while maintaining sub-100ms inference latency. This approach avoids the overhead of load balancers or multi-model caching that could introduce latency.

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Deploy behind an Application Load Balancer with multiple ml.m5.xlarge EC2 instances running the model

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Use a SageMaker multi-model endpoint with ml.m5.large instances to cache multiple models

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Use SageMaker asynchronous inference with a large batch size

Question 14

A data scientist is building a RAG application using Amazon Bedrock Knowledge Bases. They want to ensure that only the most semantically relevant documents are retrieved for each query. Which embedding model characteristic is MOST important for this requirement?

Accepted Answer

High embedding dimension. Semantic relevance depends on the quality of embeddings. High-dimensional embeddings (e.g., 1024-dim) can capture finer semantic nuances compared to lower dimensions, leading to better retrieval accuracy. Training data diversity, inference latency, and context window length are secondary factors.

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Large training dataset size

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Long context window

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Low inference latency

Question 15

A company needs to ensure that model inference endpoints in SageMaker are only accessible from a private subnet in their VPC, and no traffic goes over the public internet. Which network configuration should they use?

Accepted Answer

Use a VPC interface endpoint for SageMaker and disable public access. A VPC-only endpoint (interface endpoint) or private endpoint without public internet access ensures traffic stays within the VPC.

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Create a public endpoint and restrict access using IAM policies

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Use AWS PrivateLink for the endpoint, but keep public endpoint enabled

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Deploy the endpoint in a public subnet with a security group blocking all inbound traffic