CERT AIF-C01 GUIDE & AIF-C01 LATEST EXAM REVIEW

Cert AIF-C01 Guide & AIF-C01 Latest Exam Review

Cert AIF-C01 Guide & AIF-C01 Latest Exam Review

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Tags: Cert AIF-C01 Guide, AIF-C01 Latest Exam Review, New AIF-C01 Real Exam, Reliable AIF-C01 Practice Materials, Online AIF-C01 Test

Candidates who become Amazon AIF-C01 certified demonstrate their worth in the Amazon field. AIF-C01 certification is proof of their competence and skills. This is a highly sought after credential and it makes career advancement easier for the candidate. To become Amazon AIF-C01 Certified, you must pass the AWS Certified AI Practitioner (AIF-C01) Exam. For this task, you need actual and updated AIF-C01 Questions.

Immediately after you have made a purchase for our AIF-C01 practice dumps, you can download our AIF-C01 study materials to make preparations. It is universally acknowledged that time is a key factor in terms of the success. The more time you spend in the preparation for AIF-C01 Training Materials, the higher possibility you will pass the exam. And with our AIF-C01 study torrent, you can get preparations and get success as early as possible.

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Pass Guaranteed 2025 Professional AIF-C01: Cert AWS Certified AI Practitioner Guide

In the 21 Century, the AIF-C01 certification became more and more recognized in the society because it represented the certain ability of examinees. However, in order to obtain AIF-C01 certification, you have to spend a lot of time preparing for the AIF-C01 Exam. Many people gave up because of all kinds of difficulties before the examination, and finally lost the opportunity to enhance their self-worth. But our AIF-C01 exam questions will help you pass the exam for sure.

Amazon AWS Certified AI Practitioner Sample Questions (Q48-Q53):

NEW QUESTION # 48
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?

  • A. Batch transform
  • B. Real-time inference
  • C. Asynchronous inference
  • D. Serverless inference

Answer: B

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 References:
* 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 # 49
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?

  • A. Accuracy
  • B. Root mean squared error (RMSE)
  • C. Learning rate
  • D. R-squared score

Answer: A


NEW QUESTION # 50
A company needs to build its own large language model (LLM) based on only the company's private data.
The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

  • A. Amazon EC2 P series
  • B. Amazon EC2 C series
  • C. Amazon EC2 Trn series
  • D. Amazon EC2 G series

Answer: C

Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
* Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
* Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
* Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics- intensive applications but does not focus on minimizing environmental impact for training.
* Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner References:
* AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost- effective solution for ML model training.


NEW QUESTION # 51
A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

  • A. Observer bias
  • B. Confirmation bias
  • C. Sampling bias
  • D. Measurement bias

Answer: C

Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
* Sampling Bias:
* Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
* In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
* Why Option B is Correct:
* Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
* Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
* Why Other Options are Incorrect:
* A. Measurement bias: Involves errors in data collection or measurement, not sampling.
* C. Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
* D. Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.


NEW QUESTION # 52
A company is building an ML model to analyze archived dat
a. 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?

  • A. Real-time inference
  • B. Batch transform
  • C. Asynchronous inference
  • D. Serverless inference

Answer: B

Explanation:
I'll continue with more questions shortly. Stay tuned!


NEW QUESTION # 53
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