AIF-C01 Practice Test 37 — 15 Questions

Question 1

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

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

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Fine-tune a base LLM on the policy documents monthly

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Train a custom model from scratch on the policy documents each month

Question 2

Refer to the exhibit. A SageMaker real-time endpoint is experiencing increasing latency and memory errors after running for a few hours. What is the most likely cause and recommended fix?

Accepted Answer

Use SageMaker Debugger to identify and fix a memory leak in the inference code. The symptoms—increasing latency and memory errors after running for a few hours—point to a memory leak in the inference code. SageMaker Debugger can monitor system metrics like memory utilization and detect anomalies, helping to identify the root cause of the leak. Fixing the memory leak directly resolves the progressive degradation, whereas scaling or auto-scaling only masks the symptom.

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Scale the endpoint to a larger instance type, such as ml.r5.large

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Enable auto-scaling to add instances during high load

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Use SageMaker Model Monitor to detect data drift

Question 3

A data scientist is using a foundation model to summarize long documents. Which TWO of the following steps are most likely to improve the quality of the summaries?

Accepted Answer

Break the input document into chunks and summarize each chunk separately.. Foundation models have a fixed maximum context window (e.g., 4,096 tokens for GPT-3.5). By breaking a long document into smaller chunks and summarizing each independently, you avoid truncation and ensure the model can process the entire content without losing information. This chunking strategy is a standard preprocessing technique for handling documents that exceed the model's context length.

Answer

Use a high temperature parameter to increase creativity.

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Use a low frequency penalty to reduce repetition.

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Use a longer context length by increasing the max tokens parameter.

Question 4

A team deployed a text generation model on Amazon Bedrock. They want to monitor for toxic content in model outputs. Which evaluation approach is MOST effective?

Accepted Answer

Use Amazon Bedrock Model Evaluation with toxicity metrics. Amazon Bedrock Model Evaluation with toxicity metrics is the most effective approach because it provides automated, built-in evaluation of model outputs for toxic content using predefined metrics, directly integrated with the Bedrock service. This eliminates the need for manual effort or custom filtering, ensuring consistent and scalable monitoring of harmful content.

Answer

Enable CloudWatch Logs and set a metric filter for toxic words

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Use Amazon SageMaker Ground Truth for human annotation

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Manually review a sample of outputs each week

Question 5

A company is developing an AI system that generates news articles. To comply with transparency regulations, they must clearly indicate when content is AI-generated. Which action should they take?

Accepted Answer

Include a disclaimer in the article metadata and a visible label stating 'This content was generated by AI'. Transparency requirements often mandate disclosure that content is AI-generated, which can be done through labels, watermarks, or metadata. Providing a clear disclosure to users is the most direct and compliant approach.

Answer

Register the model with a government agency

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Remove any identifiable information about the model from the output

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Use a watermark that is invisible to users

Question 6

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. 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.

Answer

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 7

A company is using Amazon Comprehend to analyze customer feedback. They need to ensure that the documents are encrypted at rest. What should they do?

Accepted Answer

No action is needed; Amazon Comprehend automatically encrypts data at rest using AES-256. Amazon Comprehend automatically encrypts all data at rest using AES-256 encryption by default, with no additional configuration required. This encryption covers both the documents processed by the service and any models or artifacts stored internally. Therefore, no action is needed from the customer to enable encryption at rest.

Answer

Enable encryption using AWS KMS in the Comprehend console

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Store documents in an encrypted S3 bucket and use a VPC endpoint

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Use SSL/TLS for all API calls to Comprehend

Question 8

A team trained a deep learning model that achieves 99% accuracy on training data but only 70% on validation data. What is the most likely issue?

Accepted Answer

Overfitting. The model performs exceptionally well on training data (99% accuracy) but significantly worse on validation data (70% accuracy). This large gap indicates the model has memorized the training data, including noise and irrelevant patterns, rather than learning generalizable features — a classic symptom of overfitting.

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Underfitting

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Data leakage

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Feature scaling

Question 9

Which THREE of the following are capabilities of Amazon SageMaker? (Select THREE.)

Accepted Answer

Real-time inference endpoints. Amazon SageMaker provides real-time inference endpoints that allow you to deploy trained models to a fully managed HTTPS endpoint for low-latency predictions. These endpoints automatically scale based on traffic and support A/B testing, making them suitable for production workloads.

Answer

On-premises training only

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Can only deploy models to EC2 instances

Question 10

A company fine-tunes a foundation model on SageMaker using a custom dataset. They notice the training job takes too long. Which optimization technique is specifically designed to reduce training time for foundation models?

Accepted Answer

Distributed training using SageMaker Data Parallelism. SageMaker Data Parallelism distributes the training workload across multiple GPUs or instances, splitting the data and synchronizing gradients using optimized all-reduce algorithms. This specifically reduces training time for large foundation models by enabling parallel computation, which is the most direct technique for accelerating training at scale.

Answer

Using a smaller instance type

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Using Spot Instances

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Reducing batch size

Question 11

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is a native feature of Amazon Bedrock designed specifically to implement safety controls, content filters, and topic policies for foundation models like Claude. It allows developers to define denied topics, filter harmful content (e.g., hate speech, violence), and redact sensitive information, directly addressing the need to prevent harmful or biased responses in a chatbot built on Bedrock.

Answer

AWS WAF

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Amazon GuardDuty

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AWS Shield

Question 12

A company uses Amazon Bedrock Guardrails to filter harmful content. They want to ensure that the model does not generate responses containing specific keywords related to their internal project names. Which Guardrails component should they configure?

Accepted Answer

Word filters. Word filters in Bedrock Guardrails allow you to specify custom words or phrases that should be blocked in model responses.

Answer

Harmful content filters

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Topic restrictions

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Grounding checks

Question 13

A company wants to use AWS services to process natural language text. Which TWO AWS services provide natural language processing (NLP) capabilities? (Select TWO.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights such as entities, key phrases, sentiment, and syntax from text. Amazon Lex provides NLP capabilities through automatic speech recognition (ASR) and natural language understanding (NLU) to build conversational interfaces like chatbots.

Answer

Amazon Translate

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Amazon Rekognition

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Amazon Polly

Question 14

A company is using Amazon SageMaker to train a large language model with hundreds of billions of parameters. The model does not fit into the memory of a single GPU. Which approach should they use to train the model efficiently?

Accepted Answer

Use SageMaker's model parallelism strategy with the SageMaker distributed training library. SageMaker's model parallelism strategy with the SageMaker distributed training library is specifically designed for training large models that do not fit into the memory of a single GPU. It partitions the model layers across multiple GPUs, enabling efficient training of models with hundreds of billions of parameters by overlapping computation and communication.

Answer

Use a larger instance with more GPU memory, such as p4d.24xlarge

Answer

Use SageMaker's data parallelism strategy

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Reduce the model size by pruning layers until it fits into memory

Question 15

A developer is building an application that uses Amazon Bedrock to answer questions based on a large internal knowledge base. The knowledge base contains PDFs, Word documents, and web pages. Which TWO AWS services are commonly used together to implement a Retrieval-Augmented Generation (RAG) architecture on AWS? (Select TWO.)

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases is correct because it provides a fully managed service that ingests, chunks, and indexes documents (PDFs, Word, web pages) into a vector store, enabling retrieval-augmented generation (RAG) without custom infrastructure. It integrates directly with foundation models in Bedrock to retrieve relevant context and generate answers grounded in the knowledge base.

Answer

Amazon SageMaker Ground Truth

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AWS Glue

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Amazon Athena