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[General] AIP-210試験攻略 & AIP-210日本語版

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【General】 AIP-210試験攻略 & AIP-210日本語版

Posted at yesterday 22:55      View：6 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. JpexamがGoogle Driveで共有している無料かつ新しいAIP-210ダンプ：https://drive.google.com/open?id=1j_FmBrpMie13HZQWXQZ79TXyFTkLCztH
弊社のAIP-210問題集は大好評を博しました。専門家たちの整理と分析を通して、問題集の質量はよくなりました。だから、お客様は我々のAIP-210問題集を安心で利用することができます。弊社の商品の質量に疑問がありましたら、我々のサイトで無料のAIP-210デモをダウンロードして見ることができます。
AIP-210試験はJpexamの教材を準備し、高品質で合格率が高く、実際の試験を十分に理解しており、AIP-210学習教材を長年にわたって作成した専門家によって完了します。彼らは、AIP-210試験の準備をするときに受験者が本当に必要とするものを非常によく知っています。また、実際のAIP-210試験の状況を非常によく理解しています。実際の試験がどのようなものかをお知らせします。AIP-210試験問題のソフトバージョンを試すことができます。これにより、実際の試験をシミュレートできます。
試験AIP-210試験攻略 & 完璧なAIP-210日本語版 | 大人気AIP-210試験関連情報AIP-210学習ガイドは多くの利点を高め、購入する価値があります。購入する前に、AIP-210試験トレントを無料でダウンロードして試用できます。CertNexus製品を購入したら、すぐにAIP-210学習資料をダウンロードできます。 5〜10分以内に製品を郵送します。古いクライアントには無料のアップデートと割引を提供します。 AIP-210試験の教材は高い合格率を高めます。 AIP-210の学習準備には時間と労力がほとんどかからず、主に仕事やその他の重要なことに専念できます。
CertNexus Certified Artificial Intelligence Practitioner (CAIP) 認定 AIP-210 試験問題 (Q12-Q17):質問 # 12
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?
  • A. k-means, because this is a clustering problem with a small number of features.
  • B. A decision tree algorithm, because the problem is a classification problem with a small number of features.
  • C. Logistic regression, because this is a classification problem and our data is linearly separable.
  • D. Neural network model, because this is a classification problem with a large number of features.
正解：D
解説：
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]

質問 # 13
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
  • A. Clustering
  • B. Polynomial regression
  • C. Linear regression
  • D. Ridge regression
正解：D
解説：
Ridge regression is a type of regularization technique that can help reduce collinearity among independent variables. It does this by adding a penalty term to the ordinary least squares (OLS) objective function, which shrinks the coefficients of highly correlated variables towards zero. This reduces the variance of the coefficient estimates and improves the stability and accuracy of the regression model.
References: Multicollinearity in Regression Analysis: Problems, Detection, and Solutions - Statistics By Jim, A Beginner's Guide to Collinearity: What it is and How it affects our regression model - StrataScratch

質問 # 14
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
  • A. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
  • B. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
  • C. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
  • D. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
正解：B
解説：
Explanation
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.

質問 # 15
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?
  • A. The model is overfitted and trained on a low quantity of patient records.
  • B. The model is underfitted and trained on a high quantity of patient records.
  • C. The model is overfitted and trained on a high quantity of patient records.
  • D. The model is underfitted and trained on a low quantity of patient records.
正解：A
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 # 16
Which of the following is the definition of accuracy?
  • A. True Positives / (True Positives + False Positives)
  • B. True Positives / (True Positives + False Negatives)
  • C. (True Positives + False Positives) / Total Predictions
  • D. (True Positives + True Negatives) / Total Predictions
正解：D
解説：
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 # 17
......
教材をシミュレートするAIP-210のページでは、サンプルの質問であるデモを提供しています。デモを提供する目的は、お客様にトピックの私たちの部分を理解してもらうことと、それが開かれたときの学習資料の形式は何ですか？私たちの考えでは、これら2つのことは、AIP-210試験に関心のあるお客様が最も心配しているということです。製品ページにアクセスできるクリック可能なWebサイトであるソフトウェアを提供します。 AIP-210試験でマークされた赤いボックスはデモです。 PDFバージョンを無料でダウンロードでき、3つの形式すべてをクリックして表示できます。
AIP-210日本語版: https://www.jpexam.com/AIP-210_exam.html
AIP-210試験準備は専門家によって作成され、お客様がAIP-210試験に合格し、短時間で証明書を取得するのに非常に役立ちます、AIP-210参考資料を使用したお客様からいい評価をもらいました、CertNexus AIP-210試験攻略 どちらを受験したいですか、その後、候補者はリンクを開いてログインし、AIP-210テストトレントを使用してすぐに学習できます、我々のAIP-210問題集のデモを見て、あなたの需要に満たして、我々の問題集を入手すると信じています、AIP-210試験準備問題は、候補者が正しい指示を持って役に立つ努力をするのを助けることができます、CertNexus AIP-210試験攻略 これは、教材の合格率が高いためです。
わかりました、彼は、平均的な人よりもはるかに敏感な嗅覚を持っています、AIP-210試験準備は専門家によって作成され、お客様がAIP-210試験に合格し、短時間で証明書を取得するのに非常に役立ちます、AIP-210参考資料を使用したお客様からいい評価をもらいました。
自信満々とCertNexus AIP-210認定試験を受験しようどちらを受験したいですか、その後、候補者はリンクを開いてログインし、AIP-210テストトレントを使用してすぐに学習できます、我々のAIP-210問題集のデモを見て、あなたの需要に満たして、我々の問題集を入手すると信じています。
さらに、Jpexam 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=1j_FmBrpMie13HZQWXQZ79TXyFTkLCztH
https://www.fast2test.com
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