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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Real Exam Qty: | 80 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple choice, Multiple response |
| Passing Score: | 700 / 1000 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning Engineer - Associate AWS Certified Data Engineer - Associate |
| Exam Price: | USD 100 |
| Exam Duration: | 90 minutes |
| Available Languages: | English, Simplified Chinese, Portuguese (Brazil), Japanese, Korean |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 167
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Answer:
Explanation:
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
NEW QUESTION # 168
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.)
* Computer vision
* Natural language processing (NLP)
* Reinforcement learning
* Time series forecasting
Answer:
Explanation:
Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP)
* NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision
* Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting
* Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
NEW QUESTION # 169
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?
Answer: C
Explanation:
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
* Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production. This option does not fully meet the requirements.
* Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
* Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality. Building a new model may not be necessary if retraining can address the issue.
* Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 170
A company is building an AI application to automate business processes. The company uses a foundation model (FM) to support the application.
The company needs to select datasets to assess the quality of the AI model's behavior.
Which type of datasets will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI and generative AI evaluation guidance emphasizes that assessing the quality and behavior of a foundation model requires representative and diverse evaluation datasets. These datasets should reflect real-world usage patterns, edge cases, and multiple business scenarios to properly evaluate how the model behaves in production.
Using diverse datasets that cover various use cases and usage scenarios allows organizations to:
Evaluate robustness and generalization of the FM
Identify failure modes, bias, and unsafe behavior across different inputs Validate that the model performs consistently across business workflows Why the other options are incorrect:
A may remove meaningful real-world patterns and does not reflect realistic usage.
B risks reinforcing model biases and does not provide independent evaluation.
D does not reflect real or meaningful business scenarios and can distort evaluation results.
AWS AI Study Guide Reference:
AWS Responsible AI evaluation practices
AWS guidance on foundation model testing and validation
NEW QUESTION # 171
A company needs to classify equipment types from images. The company has text descriptions and sample images for each equipment type.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Multimodal foundation models process multiple types of information, including text and images, within a unified inference workflow.
Amazon Bedrock provides access to supported multimodal models that can analyze images together with textual instructions.
For equipment classification, the company can provide an equipment image and descriptions of potential equipment categories. The model can then evaluate the visual information against the descriptions and identify the most appropriate category.
This solution avoids developing, training, deploying, and maintaining a separate machine learning model.
Option A requires custom model training. Option C introduces data augmentation and fine-tuning activities.
Option D creates additional complexity through synthetic image generation and model training.
Using an existing multimodal foundation model through Amazon Bedrock therefore provides the least operational overhead, provided the chosen model supports image understanding and achieves acceptable classification accuracy.
NEW QUESTION # 172
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