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Title: AIP-210全真問題集、AIP-210資格トレーリング [Print This Page]
Author: raygray641    Time: 5 day before
Title: AIP-210全真問題集、AIP-210資格トレーリング
さらに、JPTestKing AIP-210ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1Sq6jwb3SFiXi2RmTnCGrEKbMszk3mdLj
この驚くほど高く受け入れられている試験に適合するには、AIP-210学習教材のような上位の実践教材で準備する必要があります。彼らは時間とお金の面で最良の選択です。 AIP-210トレーニング準備のすべての内容は、素人にfされているのではなく、この分野のエリートによって作成されています。弊社の優秀なヘルパーによる効率に魅了された数万人の受験者を引き付けたリーズナブルな価格に沿ってみましょう。難しい難問は、AIP-210クイズガイドで解決します。
CertNexus AIP-210 認定試験の出題範囲：
トピック出題範囲
トピック 1
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
トピック 2
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
トピック 3
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
トピック 4
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
トピック 5
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
トピック 6
  • Identify potential ethical concerns
  • Analyze machine learning system use cases

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) 認定 AIP-210 試験問題 (Q80-Q85):質問 # 80
Which of the following approaches is best if a limited portion of your training data is labeled?
正解：B
解説：
Explanation
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled.
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. 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. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.

質問 # 81
Which of the following is the definition of accuracy?
正解：C
解説：
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).

質問 # 82
Which of the following tests should be performed at the production level before deploying a newly retrained model?
正解：B
解説：
Explanation
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. References: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]

質問 # 83
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?
正解：D

質問 # 84
A healthcare company experiences a cyberattack, where the hackers were able to reverse-engineer a dataset to break confidentiality.
Which of the following is TRUE regarding the dataset parameters?
正解：C
解説：
Explanation
Overfitting is a problem that occurs when a model learns too much from the training data and fails to generalize well to new or unseen data. Overfitting can result from using a low quantity of training data, a high complexity of the model, or a lack of regularization. Overfitting can also increase the risk of reverse-engineering a dataset from a model's outputs, as the model may reveal too much information about the specific features or patterns of the training data. This can break the confidentiality of the data and expose sensitive information about the individuals in the dataset .

質問 # 85
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