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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Passing Score: | 700/1000 |
| Exam Price: | $100 USD |
| Available Languages: | Spanish, Korean, English, Japanese, Simplified Chinese, Portuguese (Brazil) |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple choice, Multiple response |
| Recommended Training: | AWS Certified AI Practitioner Official Exam Guide AWS Skill Builder - AI Practitioner Learning Plan |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 182
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?
Answer: B
Explanation:
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.
NEW QUESTION # 183
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.
Answer:
Explanation:
Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data
NEW QUESTION # 184
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 185
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?
Answer: A
Explanation:
In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.
* Option B (Correct): "Hallucination": This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.
* Option A: "Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.
* Option C: "Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.
* Option D: "Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.
AWS AI Practitioner References:
* Large Language Models on AWS: AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.
NEW QUESTION # 186
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
Answer: A
NEW QUESTION # 187
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