AIF-C01 Exam Questions Tutorials

The correct answer is D – Purchase Provisioned Throughput on Amazon Bedrock, which provides guaranteed and predictable model capacity for workloads with consistent or steady request volume. According to AWS Bedrock documentation, Provisioned Throughput is specifically designed for production applications that require reliable, consistent inference performance at a controlled cost. It allows customers to reserve a fixed number of model inference units (MIUs), ensuring low latency and cost savings compared to on-demand pricing when traffic is steady. On-demand throughput (option B) is ideal for unpredictable or sporadic usage, but it becomes more expensive for stable traffic patterns because it charges per token with no discount for steady volume. Hosting the model on EC2 (option A) or Lambda (option C) increases operational overhead, requires model containerization, scaling management, and may not support LLM-level GPU performance efficiently. Bedrock Provisioned Throughput eliminates infrastructure management and provides the most cost-effective solution for stable, predictable workloads.

Referenced AWS Documentation:

Amazon Bedrock Developer Guide – Provisioned Throughput

AWS ML Specialty Study Guide – Cost Optimization for Generative AI

Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.

D is correct:

"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions."

(Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)

A (JumpStart) provides prebuilt solutions and models, not monitoring.

B (HyperPod) is for large-scale training, not model monitoring.

C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.

The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs."

(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)

Detailed Explanation:

Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.

Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.

Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.

Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.

Step 1: Store meeting audio recordings in an Amazon S3 bucket.

Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

Step 3: Summarize meeting text files by using Amazon Bedrock.

The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.

Exact Extract from AWS AI Documents:

From the Amazon Transcribe Developer Guide:

"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket."

From the AWS Bedrock User Guide:

"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts."

(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization)

Detailed Explanation:

Step 1: Store meeting audio recordings in an Amazon S3 bucket.Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.

Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.

Step 3: Summarize meeting text files by using Amazon Bedrock.Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application’s requirements.

Unused Options Analysis:

Convert meeting audio recordings to meeting text files by using Amazon Polly.Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.

Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.

Summarize meeting text files by using Amazon Lex.Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.

Hotspot Selection Analysis:

The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps—storing in S3, converting with Transcribe, and summarizing with Bedrock—form a complete and logical workflow for the application.