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Pass Guaranteed CertNexus - AIP-210 - CertNexus Certified Artificial Intelligence Practitioner (CAIP)–High Pass-Rate Training Tools

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following is the correct definition of the quality criteria that describes completeness?

A. The degree to which a set of measures are equivalent across systems.
B. The degree to which the measures conform to defined business rules or constraints.
C. The degree to which all required measures are known.
D. The degree to which a set of measures are specified using the same units of measure in all systems.

Answer: C

Explanation:
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.

NEW QUESTION # 73
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. Values near 0.5 before logit-transformation will be near 0 after.
B. After logit-transformation, the data may violate the assumption of independence.
C. Noisy data could become more influential in your model.
D. The model will be more likely to violate the assumption of normality.

Answer: C

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 # 74
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?

A. Technical
B. Automation
C. Emergent
D. Preexisting

Answer: D

Explanation:
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.

NEW QUESTION # 75
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?

A. Delete entire columns that contain any missing features.
B. Delete entire rows that contain any missing features.
C. Fill in missing features with random values for that feature in the training set.
D. Fill in missing features with the average of observed values for that feature in the entire dataset.

Answer: D

Explanation:
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.

NEW QUESTION # 76
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?

A. High positive predictive value
B. Low false positive rate
C. Low false negative rate
D. High negative predictive value

Answer: C

Explanation:
Explanation
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.

NEW QUESTION # 77
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