Get Amazon AIF-C01 Exam Practice Questions - Real and Updated

Answer : A

Object detection is the correct strategy for automatically identifying and categorizing animals in photos.

Object Detection:

A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.

Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.

Why Option A is Correct:

Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.

Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.

Why Other Options are Incorrect:

B . Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.

C . Named entity recognition: Used in NLP to identify entities in text, not for image processing.

D . Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.

Answer : A

AWS Glue is a serverless data integration service that enables users to extract, transform, and load (ETL) data. For a company with minimal programming experience, Amazon Q Developer provides an AI-powered assistant that can generate code, explain AWS services, and guide users through tasks like creating AWS Glue jobs. This makes it an ideal tool to help the company use AWS Glue effectively.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Q Developer:

'Amazon Q Developer is an AI-powered assistant that helps developers by generating code, answering questions about AWS services, and providing step-by-step guidance for tasks such as building ETL pipelines with AWS Glue. It is designed to assist users with varying levels of expertise, including those with minimal programming experience.'

(Source: AWS Documentation, Amazon Q Developer Overview)

Detailed

Option A: Amazon Q Developer

This is the correct answer. Amazon Q Developer can assist the company by generating AWS Glue scripts, explaining Glue concepts, and providing guidance on setting up ETL jobs, which is particularly helpful for users with limited programming experience.

Option B: AWS Config

AWS Config is used for tracking and managing resource configurations and compliance, not for assisting with coding or using services like AWS Glue. This option is incorrect.

Option C: Amazon Personalize

Amazon Personalize is a machine learning service for building recommendation systems, not for assisting with data integration or AWS Glue. This option is irrelevant.

Option D: Amazon Comprehend

Amazon Comprehend is an NLP service for analyzing text, not for helping users write code or use AWS Glue. This option does not meet the requirements.

AWS Documentation: Amazon Q Developer Overview (https://aws.amazon.com/q/developer/)

AWS Glue Developer Guide: Introduction to AWS Glue (https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html)

AWS AI Practitioner Learning Path: Module on AWS Developer Tools and Services

Answer : B

Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.

Unsupervised Learning:

This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data.

In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.

Why Option B is Correct:

Handling Unlabeled Data: Unsupervised learning is specifically designed to work with unlabeled data, making it ideal for the company's need to classify customer data.

Customer Segmentation: Techniques in unsupervised learning can be used to find natural groupings within customer data, such as identifying high-value vs. low-value customers or segmenting based on purchasing behavior.

Why Other Options are Incorrect:

A . Supervised learning: Requires labeled data with input-output pairs to train the model, which is not suitable since the company's data is unlabeled.

C . Reinforcement learning: Focuses on training an agent to make decisions by maximizing some notion of cumulative reward, which does not align with the company's need for customer classification.

D . Reinforcement learning from human feedback (RLHF): Similar to reinforcement learning but involves human feedback to refine the model's behavior; it is also not appropriate for classifying unlabeled customer data.

Answer : A

The company is building a chatbot using an LLM to answer questions about HR policies, with access to a large digital documentation base. Retrieval Augmented Generation (RAG) optimizes the LLM's responses by retrieving relevant information from the documentation base and using it to generate accurate, contextually grounded answers, reducing hallucinations and improving response quality.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

'Retrieval Augmented Generation (RAG) enhances the performance of large language models by retrieving relevant information from external knowledge bases, such as documentation or databases, and incorporating it into the generation process. This technique ensures responses are accurate and grounded in the provided data, making it ideal for applications like policy chatbots.'

(Source: AWS Bedrock User Guide, Retrieval Augmented Generation)

Detailed

Option A: Use Retrieval Augmented Generation (RAG).This is the correct answer. RAG leverages the documentation base to provide the LLM with relevant HR policy information, optimizing the chatbot's responses for accuracy and relevance.

Option B: Use few-shot prompting.Few-shot prompting provides a few examples in the prompt to guide the LLM, but it is less effective than RAG for large documentation bases, as it cannot dynamically retrieve specific policy details.

Option C: Set the temperature to 1.Setting the temperature to 1 controls the randomness of the LLM's output but does not optimize responses using external documentation. This option is unrelated to the documentation base.

Option D: Decrease the token size.Decreasing token size (likely referring to limiting input/output tokens) may reduce response length but does not optimize the quality of responses using the documentation base.

AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/rag.html)

AWS AI Practitioner Learning Path: Module on Generative AI Optimization

Amazon Bedrock Developer Guide: Building Policy Chatbots (https://aws.amazon.com/bedrock/)

Answer : A

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.

Option A (Correct): 'Real-time inference': This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.

Option B: 'Serverless inference' is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.

Option C: 'Asynchronous inference' is incorrect because it is used for workloads that do not require immediate responses.

Option D: 'Batch transform' is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.

AWS AI Practitioner Reference:

Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.