At ValidExamDumps, we consistently monitor updates to the Amazon AIF-C01 exam questions by Amazon. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Amazon AWS Certified AI Practitioner exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Amazon in their Amazon AIF-C01 exam. These outdated questions lead to customers failing their Amazon AWS Certified AI Practitioner exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Amazon AIF-C01 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.
Option B (Correct): 'Accuracy': This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.
Option A: 'R-squared score' is incorrect as it is used for regression analysis, not classification tasks.
Option C: 'Root mean squared error (RMSE)' is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.
Option D: 'Learning rate' is incorrect as it is a hyperparameter for training, not a performance metric.
AWS AI Practitioner Reference:
Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.
What does an F1 score measure in the context of foundation model (FM) performance?
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. Reference: AWS Foundation Models Guide.
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?
Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.
Amazon SageMaker Feature Store:
A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.
It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.
Why Option A is Correct:
Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams.
Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.
Why Other Options are Incorrect:
B . SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store.
C . SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams.
D . SageMaker Model Cards: Provide model documentation, not feature management.
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): 'Detect imbalances or disparities in the data': This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): 'Evaluate the model's behavior so that the company can provide transparency to stakeholders': This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: 'Ensure that the model runs frequently' is incorrect because the frequency of model runs does not address bias.
Option D: 'Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate' is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: 'Ensure that the model's inference time is within the accepted limits' is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner Reference:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.
A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?
Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.
Option A (Correct): 'Batch transform': This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.
Option B: 'Real-time inference' is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.
Option C: 'Serverless inference' is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.
Option D: 'Asynchronous inference' is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.
AWS AI Practitioner Reference:
Batch Transform on AWS SageMaker: AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.
