無料PDFAIP-210試験勉強過去問 | 最初の試行で簡単に勉強して試験に合格する & 信頼できるCertNexus CertNexus Certified Artificial Intelligence Practitioner (CAIP)CertNexusのAIP-210試験のために不安なのですか。弊社のソフトは買うたるかどうかまだ疑問がありますか。そうであれば、無料で弊社の提供するCertNexusのAIP-210のデモをダウンロードしてみよう。我々提供する資料はあなたの需要だと知られています。あなたのCertNexusのAIP-210試験に参加する圧力を減ってあなたの効率を高めるのは我々の使命だと思います。 CertNexus Certified Artificial Intelligence Practitioner (CAIP) 認定 AIP-210 試験問題 (Q45-Q50):質問 # 45
Which of the following scenarios is an example of entanglement in ML pipelines?
A. Change the way output is visualized in the monitoring step.
B. Add a new method for drift detection in the model evaluation step.
C. Add a new pipeline for retraining the model in the model training step.
D. Change in normalization function in the feature engineering step.
正解：D
解説：
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.
質問 # 46
Which of the following describes a typical use case of video tracking?
A. Medical diagnosis
B. Augmented dreaming
C. Traffic monitoring
D. Video composition
正解：C
解説：
Video tracking is a technique that involves detecting and following moving objects in a video sequence.
Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human- computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.
質問 # 47
Which of the following principles supports building an ML system with a Privacy by Design methodology?
A. Understanding, documenting, and displaying data lineage.
B. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
C. Avoiding mechanisms to explain and justify automated decisions.
D. Collecting and processing the largest amount of data possible.
正解：A
解説：
Explanation
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .
質問 # 48
You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
A. Random forest
B. Ridge regression
C. Deep learning neural network
D. Support-vector machine
正解：B
解説：
Explanation
Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.
質問 # 49
You create a prediction model with 96% accuracy. While the model's true positive rate (TPR) is performing well at 99%, the true negative rate (TNR) is only 50%. Your supervisor tells you that the TNR needs to be higher, even if it decreases the TPR. Upon further inspection, you notice that the vast majority of your data is truly positive.
What method could help address your issue?
A. Quality filtering
B. Oversampling
C. Normalization
D. Principal components analysis
正解：B
解説：
Explanation
Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate. References: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis - Wikipedia]