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[Hardware] AIP-210 Exam Testking | AIP-210 Questions Pdf

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【Hardware】 AIP-210 Exam Testking | AIP-210 Questions Pdf

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 2
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 3
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 4
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q26-Q31):NEW QUESTION # 26
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
  • A. The model will be more likely to violate the assumption of normality.
  • B. After logit-transformation, the data may violate the assumption of independence.
  • C. Values near 0.5 before logit-transformation will be near 0 after.
  • D. Noisy data could become more influential in your model.
Answer: D
Explanation:
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients.
References: [Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]

NEW QUESTION # 27
For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy. Which is the best algorithm for this use case?
  • A. Deep neural network
  • B. SVM
  • C. K-nearest neighbors
  • D. Random forest
Answer: D
Explanation:
Explanation
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature's relative contribution to the model's accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .

NEW QUESTION # 28
In which of the following scenarios is lasso regression preferable over ridge regression?
  • A. The number of features is much larger than the sample size.
  • B. The sample size is much larger than the number of features.
  • C. There are many features with no association with the dependent variable.
  • D. There is high collinearity among some of the features associated with the dependent variable.
Answer: C
Explanation:
Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.

NEW QUESTION # 29
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: D
Explanation:
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.

NEW QUESTION # 30
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. Oversampling
  • B. Normalization
  • C. Principal components analysis
  • D. Quality filtering
Answer: A
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]

NEW QUESTION # 31
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