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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Format: | Multiple choice, Ordering, Multiple response |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Exam Duration: | 90 minutes |
| Available Languages: | Japanese, Simplified Chinese, Traditional Chinese, Korean, English |
| Exam Price: | 100 USD |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Passing Score: | 700 (scaled score 100–1000) |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | AWS Certification Registration Pearson VUE Scheduling |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
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NEW QUESTION # 218
A financial company is using ML to help with some of the company's tasks.
Which option is a use of generative AI models?
Answer: B
Explanation:
Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text-like customer complaints-is a classic application of generative AI.
A is correct:
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases)
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases) B and C are standard ML classification/segmentation tasks.
D is a regression/prediction task, not generative.
NEW QUESTION # 219
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Answer: C
Explanation:
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.
NEW QUESTION # 220
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
Answer: B
NEW QUESTION # 221
Which AWS service or feature stores embeddings In a vector database for use with foundation models (FMs) and Retrieval Augmented Generation (RAG)?
Answer: A
NEW QUESTION # 222
A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.
Which evaluation metric meets these requirements?
Answer: A
Explanation:
Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.
C is correct:
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide)
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide) A (Recall) measures how many actual frauds are caught, but does not minimize false positives.
B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).
NEW QUESTION # 223
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