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Exam Questions AIP-210 Vce & AIP-210 Training Materials

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q81-Q86):

NEW QUESTION # 81
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. Normalization
C. Oversampling
D. Principal components analysis

Answer: C

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 # 82
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. Noisy data could become more influential in your model.
B. After logit-transformation, the data may violate the assumption of independence.
C. The model will be more likely to violate the assumption of normality.
D. Values near 0.5 before logit-transformation will be near 0 after.

Answer: A

Explanation:
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 # 83
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

A. Bag of words model with TF-IDF
B. Bag of bigrams (2 letter pairs)
C. Word2Vec algorithm
D. Clustering similar words and representing words by group membership

Answer: B

Explanation:
Explanation
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word "hello" would be represented as
{"he": 1, "el": 1, "ll": 1, "lo": 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as "th" in English or "ch" in German .

NEW QUESTION # 84
A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

A. Precision and accuracy
B. Recall and explained variance
C. Mean squared error
D. Precision and recall

Answer: D

Explanation:
Explanation
Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).

NEW QUESTION # 85
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. Random forest
C. K-nearest neighbors
D. SVM

Answer: B

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 # 86
......

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