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[General] CertNexus AIP-210日本語参考、AIP-210復習対策書

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【General】 CertNexus AIP-210日本語参考、AIP-210復習対策書

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無料でクラウドストレージから最新のCertShiken AIP-210 PDFダンプをダウンロードする：https://drive.google.com/open?id=1bR9qKhiCQmpFieEAw8oDjrbX27ktcOTm
弊社は、当社のAIP-210試験エンジンを学習ツールとして使用する方法で、候補者とのさらなる協力を目指して、大きな集中的な進歩を遂げました。 AIP-210試験軍隊により多くの人々が参加することで、私たちは国際市場でトップクラスのトレーニング資料プロバイダーになりました。 さらに、私たちは常に「相互開発と利益」の原則を順守し、学習の過程で必要なときはいつでもAIP-210実践教材がタイムリーで効果的な支援を提供できると信じています。
AIP-210実践資料は、これらのAIP-210実践資料を説明責任を持って作成した当社のものです。 また、AIP-210トレーニング資料は効率的な製品です。 さらに、AIP-210試験準備は適切で立派な練習資料です。 進捗状況を確認し、AIP-210トレーニング資料の証明書を取得することは、当然のことながら、最新かつ最も正確な知識を備えた最も専門的な専門家によるものです。 AIP-210試験準備は市場の大部分を占めています。
CertNexus AIP-210復習対策書 & AIP-210テスト問題集AIP-210の実際の学習ガイド資料は、より良いレビューを得るのに役立ちます。これは非常に直感的な標準ですが、包括的ではない場合があるため、テストAIP-210認定を取得することの重要性を知っておく必要があります。自分の能力を証明するのに十分な資格を持っている場合にのみ、厳しい現実の中で敵を打ち負かすことができます。 AIP-210の実際の質問は、AIP-210認定試験に合格し、認定資格をより迅速かつ効率的に取得するのに役立つと考えています。
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
  • 数値データとカテゴリデータを変換
  • モデルを運用する際のビジネス リスク、倫理的懸念、および関連概念に対処する
トピック 2
  • データの品質とサイズがアルゴリズム
  • 機械学習のエンジニアリング機能に与える相対的な影響を認識する
トピック 3
  • ビジネス リスク、倫理的懸念、およびトレーニングと調整における関連概念に対処する
  • テキスト、数値、音声、またはビデオ データ形式を扱う
トピック 4
  • 機械および深層学習モデルの設計
  • ML ワークフローでのデータ収集
  • 変換プロセスの説明

CertNexus Certified Artificial Intelligence Practitioner (CAIP) 認定 AIP-210 試験問題 (Q34-Q39):質問 # 34
In general, models that perform their tasks:
  • A. More accurately are less robust against adversarial attacks.
  • B. More accurately are neither more nor less robust against adversarial attacks.
  • C. Less accurately are less robust against adversarial attacks.
  • D. Less accurately are neither more nor less robust against adversarial attacks.
正解：A
解説：
Explanation
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]

質問 # 35
Which of the following pieces of AI technology provides the ability to create fake videos?
  • A. Long short-term memory (LSTM) networks
  • B. Support-vector machines (SVM)
  • C. Generative adversarial networks (GAN)
  • D. Recurrent neural networks (RNN)
正解：C
解説：
Explanation
Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.

質問 # 36
Why do data skews happen in the ML pipeline?
  • A. Test and evaluation data are designed incorrectly.
  • B. There Is a mismatch between live input data and offline data.
  • C. There is a mismatch between live output data and offline data.
  • D. There is insufficient training data for evaluation.
正解：B
解説：
Explanation
Data skews happen in the ML pipeline when the distribution or characteristics of the live input data differ from those of the offline data used for training and testing the model. This can lead to a degradation of the model performance and accuracy, as the model is not able to generalize well to new data. Data skews can be caused by various factors, such as changes in user behavior, data collection methods, data quality issues, or external events. References: What is training-serving skew in Machine Learning?, Data preprocessing for ML: options and recommendations

質問 # 37
For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the 10- year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?
  • A. Independence
  • B. Normality
  • C. Linearity
  • D. Equality of variance (Homoscedastidty)
正解：A
解説：
Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors. Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.

質問 # 38
Which of the following tests should be performed at the production level before deploying a newly retrained model?
  • A. Security test
  • B. A/Btest
  • C. Unit test
  • D. Performance test
正解：D
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 # 39
......
現在、どの領域にでも勉強して努力する必要があります。IT業界でも同じです。CertNexusに関する仕事をしている人たちはさまざまな認証試験に参加して自分の知識を補充し、よく働く必要があります。AIP-210試験に合格するのはあなたの能力を証明して、質素を高めることができます。
AIP-210復習対策書: https://www.certshiken.com/AIP-210-shiken.html
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