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[General] New AIP-210 Test Fee & AIP-210 Valid Test Braindumps

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【General】 New AIP-210 Test Fee & AIP-210 Valid Test Braindumps

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q68-Q73):NEW QUESTION # 68
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
  • A. Stereotype bias
  • B. Sampling bias
  • C. Algorithmic bias
  • D. Systematic value distortion
Answer: B
Explanation:
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]

NEW QUESTION # 69
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
  • A. Ridge regression
  • B. Logistic regression
  • C. K-nearest neighbors
  • D. Lasso regression
  • E. K-means
Answer: A,D
Explanation:
Lasso regression and ridge regression are both types of linear regression models that can handle high- dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

NEW QUESTION # 70
Which of the following statements are true regarding highly interpretable models? (Select two.)
  • A. They are usually binary classifiers.
  • B. They usually compromise on model accuracy for the sake of interpretability.
  • C. They are usually easier to explain to business stakeholders.
  • D. They are usually referred to as "black box" models.
  • E. They are usually very good at solving non-linear problems.
Answer: B,C
Explanation:
Explanation
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.

NEW QUESTION # 71
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
  • A. Model validation occurs before model evaluation.
  • B. Model validation is defined as a set of tasks to confirm the model performs as expected.
  • C. Model evaluation is defined as an external component.
  • D. Model evaluation and validation are the same.
Answer: B
Explanation:
Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics. References: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning

NEW QUESTION # 72
You and your team need to process large datasets of images as fast as possible for a machine learning task.
The project will also use a modular framework with extensible code and an active developer community.
Which of the following would BEST meet your needs?
  • A. Microsoft Cognitive Services
  • B. TensorBoard
  • C. Caffe
  • D. Keras
Answer: C
Explanation:
Explanation
Caffe is a deep learning framework that is designed for speed and modularity. It can process large datasets of images efficiently and supports various types of neural networks. It also has a large and active developer community that contributes to its code base and documentation. Caffe is suitable for image processing tasks such as classification, segmentation, detection, and recognition

NEW QUESTION # 73
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