Amazon AWS Certified AI Practitioner AIF-C01 Exam Practice Test

Answer : D

The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.

From AWS Bedrock documentation:

'When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences.'

Explanation of other options:

A . Model size affects performance and cost but doesn't directly guarantee low latency.

B . Training data quality is important for accuracy, but it doesn't address real-time performance requirements.

C . GPU availability matters in infrastructure planning, not in model selection for latency optimization.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Model Selection Guide -- Real-time Use Case Considerations

AWS ML Specialty Guide -- Foundation Model Performance Criteria

Answer : C

An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.

Anomaly Detection:

Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.

This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.

Why Option C is Correct:

Detects Suspicious Behavior: An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.

Real-time Monitoring: Provides continuous analysis of network traffic to identify potential security threats.

Why Other Options are Incorrect:

A . Speech recognition system: Is unrelated to detecting suspicious IP addresses.

B . NLP named entity recognition: Focuses on identifying entities in text, not IP address analysis.

D . Fraud forecasting system: Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.

Thus, C is the correct answer for detecting suspicious IP addresses.

Answer : C

Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. 'Amazon Q' is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.

Option C (Correct): 'Amazon Q in Amazon QuickSight': This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.

Option A, B, and D: These options are incorrect:

A . Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.

B . Amazon Q Developer: This is not an existing AWS service or feature.

D . Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.

AWS AI Practitioner Reference:

Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.

Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.

Answer : A

The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.

From AWS documentation:

'Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed.'

This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.

Explanation of other options:

B . Model training refers to the process of teaching a model from data and doesn't explain how predictions are made.

C . Model interoperability refers to the ability of systems or models to work across different platforms or environments.

D . Model performance refers to how accurate or effective the model is but doesn't relate to the explanation of its decisions.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker Clarify Documentation -- Explainability and Bias Detection

AWS Machine Learning Specialty Study Guide -- Responsible AI and Model Explainability

Answer : D

Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch predictions, allowing companies to deploy ML models without handling any servers or infrastructure management.

D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure overhead.

A and B require infrastructure management.

C (CloudFront/S3) is not for model deployment, but for static content delivery.

''Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure.''

(Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)

Answer : A

Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.

A is correct:

'Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy.'

(Reference: Amazon SageMaker Overview)

B (Amazon Personalize) is for recommendations, not general ML or waste reduction.

C (CloudWatch) is for monitoring, not ML training or deployment.

D (Rekognition) is for image/video analysis.

Answer : B

Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.

Nova Lite is optimized for lightweight, faster inference at lower cost.

Nova Canvas is a creative tool for visual design.

Nova Reel is optimized for video-related use cases.

Reference:

AWS Documentation -- Amazon Nova Models