[Jul 19, 2024] Latest Certified AI Practitioner AIP-210 Actual Free Exam Questions [Q25-Q46]

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[Jul 19, 2024] Latest Certified AI Practitioner AIP-210 Actual Free Exam Questions

Certified AI Practitioner AIP-210 Dumps Updated Practice Test and 92 unique questions

CertNexus AIP-210 Exam Syllabus Topics:

Topic	Details
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	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Topic 3	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Topic 4	Identify potential ethical concerns Analyze machine learning system use cases

Q25. Which of the following items should be included in a handover to the end user to enable them to use and run a trained model on their own system? (Select three.)

Information on the folder structure in your local machine

Intermediate data files

Link to a GitHub repository of the codebase

README document

Sample input and output data files

Q26. Which of the following pieces of AI technology provides the ability to create fake videos?

Generative adversarial networks (GAN)

Long short-term memory (LSTM) networks

Recurrent neural networks (RNN)

Support-vector machines (SVM)

Q27. 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))

After logit-transformation, the data may violate the assumption of independence.

Noisy data could become more influential in your model.

The model will be more likely to violate the assumption of normality.

Values near 0.5 before logit-transformation will be near 0 after.

Q28. Why do data skews happen in the ML pipeline?

Test and evaluation data are designed incorrectly.

There Is a mismatch between live input data and offline data.

There is a mismatch between live output data and offline data.

There is insufficient training data for evaluation.

Q29. Which of the following principles supports building an ML system with a Privacy by Design methodology?

Avoiding mechanisms to explain and justify automated decisions.

Collecting and processing the largest amount of data possible.

Understanding, documenting, and displaying data lineage.

Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.

Q30. R-squared is a statistical measure that:

Combines precision and recall of a classifier into a single metric by taking their harmonic mean.

Expresses the extent to which two variables are linearly related.

Is the proportion of the variance for a dependent variable thaf’ s explained by independent variables.

Represents the extent to which two random variables vary together.

Q31. You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

Decision tree

Logistic regression

Random forest

XGBoost

Q32. 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?

Q33. In general, models that perform their tasks:

Less accurately are less robust against adversarial attacks.

Less accurately are neither more nor less robust against adversarial attacks.

More accurately are less robust against adversarial attacks.

More accurately are neither more nor less robust against adversarial attacks.

Q34. Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?

Using TF-IDF because in translation machines, we do not care about the order of the words.

Using TF-IDF because in translation machines, we need to consider the order of the words.

Using Word2vec because in translation machines, we do not care about the order of the words.

Using Word2vec because in translation machines, we need to consider the order of the words.

Q35. Which of the following is NOT a valid cross-validation method?

Bootstrapping

K-fold

Leave-one-out

Stratification

Q36. 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?

Deep neural network

K-nearest neighbors

Random forest

SVM

Q37. You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?

When it is necessary to save computational time.

When the categories of the dependent variable are not linearly separable.

When the distribution of the dependent variable is Gaussian.

When there is high correlation among the features.

Q38. Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?

Add 1 to all of the Y values.

Divide all the Y values by the standard deviation of Y.

Explore the data for outliers.

Subtract the mean of Y from all the Y values.

Q39. A product manager is designing an Artificial Intelligence (AI) solution and wants to do so responsibly, evaluating both positive and negative outcomes.
The team creates a shared taxonomy of potential negative impacts and conducts an assessment along vectors such as severity, impact, frequency, and likelihood.
Which modeling technique does this team use?

Business

Harms

Process

Threat

Q40. Which two techniques are used to build personas in the ML development lifecycle? (Select two.)

Population estimates

Population regression

Population resampling

Population triage

Population variance

Q41. An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.
Which ethical principle is this an example of?

Fairness

Privacy

Safety

Transparency

Q42. Which of the following describes a benefit of machine learning for solving business problems?

Increasing the quantity of original data

Increasing the speed of analysis

Improving the constraint of the problem

Improving the quality of original data

Q43. 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?

Algorithmic bias

Sampling bias

Stereotype bias

Systematic value distortion

Q44. Which of the following is TRUE about SVM models?

They can be used only for classification.

They can be used only for regression.

They can take the feature space into higher dimensions to solve the problem.

They use the sigmoid function to classify the data points.

Q45. A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?

The AI model was trained in winter and applied in summer.

The application was migrated from on-premise to a public cloud.

The team set flawed expectations when training the model.

The training data used was inaccurate.

Q46. Which of the following sentences is true about model evaluation and model validation in ML pipelines?

Model evaluation and validation are the same.

Model evaluation is defined as an external component.

Model validation is defined as a set of tasks to confirm the model performs as expected.

Model validation occurs before model evaluation.