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[Hardware] AIP-210 Pdf Format & New AIP-210 Test Prep

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【Hardware】 AIP-210 Pdf Format & New AIP-210 Test Prep

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 2
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 3
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 4
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q21-Q26):NEW QUESTION # 21
When should you use semi-supervised learning? (Select two.)
  • A. Labeling data is challenging and expensive.
  • B. A small set of labeled data is biased toward one class.
  • C. There is a large amount of unlabeled data to be used for predictions.
  • D. There is a large amount of labeled data to be used for predictions.
  • E. A small set of labeled data is available but not representative of the entire distribution.
Answer: A,C
Explanation:
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
* Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance.
* There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self- training, co-training, or generative models, to incorporate unlabeled data into the learning process.

NEW QUESTION # 22
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?
  • A. When data drift is detected
  • B. Periodically every year
  • C. When concept drift is detected
  • D. Periodically before New Year's Day and after New Year's Day
Answer: B

NEW QUESTION # 23
What is the primary benefit of the Federated Learning approach to machine learning?
  • A. It protects the privacy of the user's data while providing well-trained models.
  • B. It requires less computation to train the same model using a traditional approach.
  • C. It uses large, centralized data stores to train complex machine learning models.
  • D. It does not require a labeled dataset to solve supervised learning problems.
Answer: A
Explanation:
Federated learning is a distributed approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data with each other or a central server. This protects the privacy of the user's data while still enabling well-trained models that can benefit from diverse and large-scale datasets.
References: [Federated Learning - Wikipedia], [Federated Learning for Mobile Keyboard Prediction - Google AI Blog]

NEW QUESTION # 24
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
  • A. Detect hidden attacks
  • B. Respond to hidden attacks
  • C. Recover from hidden attacks
  • D. Prevent hidden attacks
Answer: D
Explanation:
Explanation
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model's performance that may indicate a potential attack.

NEW QUESTION # 25
Which of the following metrics is being captured when performing principal component analysis?
  • A. Kurtosis
  • B. Variance
  • C. Skewness
  • D. Missingness
Answer: B
Explanation:
Explanation
Principal component analysis (PCA) is a technique that reduces the dimensionality of a dataset by transforming it into a set of new variables called principal components. The principal components are linear combinations of the original variables that capture the maximum amount of variance in the data. The first principal component explains the most variance, the second principal component explains the second most variance, and so on. The goal of PCA is to retain as much variance as possible while reducing the number of variables.

NEW QUESTION # 26
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