Use AIP-210 Dumps to Obtain CertNexus Certification

Page 1 of 3

1. Which of the following is the primary purpose of hyperparameter optimization?

Controls the learning process of a given algorithm

Makes models easier to explain to business stakeholders

Improves model interpretability

Increases recall over precision

2. Which of the following describes a typical use case of video tracking?

Augmented dreaming

Medical diagnosis

Traffic monitoring

Video composition

3. When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

Bag of words model with TF-IDF

Bag of bigrams (2 letter pairs)

Word2Vec algorithm

Clustering similar words and representing words by group membership

4. An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert the customers only when a human gets close to their house.

Which of the following algorithms is an appropriate option with a correct reason?

A decision tree algorithm, because the problem is a classification problem with a small number of features.

k-means, because this is a clustering problem with a small number of features.

Logistic regression, because this is a classification problem and our data is linearly separable.

Neural network model, because this is a classification problem with a large number of features.

5. Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)

The Beta value is the rate of type II errors for the test.

The Beta value is the rate of type I errors for the test.

The statistical power of a test is the inverse of the Beta value, or 1 - Beta.

The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.

6. A data scientist is tasked to extract business intelligence from primary data captured from the public.

Which of the following is the most important aspect that the scientist cannot forget to include?

Cyberprotection

Cybersecurity

Data privacy

Data security

7. 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

8. For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the 10-year period, with age as the dependent variable and the biomarkers as predictors.

Which assumption of linear regression is being violated?

Equality of variance (Homoscedastidty)

Independence

Linearity

Normality

9. Which of the following is a common negative side effect of not using regularization?

Overfitting

Slow convergence time

Higher compute resources

Low test accuracy

10. Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?

A more complex model

Guaranteed availability of enough space

Increase in data bandwidth consumption

Reduction in latency

Page 2 of 3

11. 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

12. Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)

Disable logging for model access.

Launch ML Instances In a virtual private cloud (VPC).

Monitor model degradation.

Use data encryption.

Use max privilege to control access to ML artifacts.

Use Secrets Manager to protect credentials.

13. 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)

14. Which of the following scenarios is an example of entanglement in ML pipelines?

Add a new method for drift detection in the model evaluation step.

Add a new pipeline for retraining the model in the model training step.

Change in normalization function in the feature engineering step.

Change the way output is visualized in the monitoring step.

15. 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.

16. A change in the relationship between the target variable and input features is

concept drift.

covariate shift.

data drift.

model decay.

17. 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?

Delete entire rows that contain any missing features.

Fill in missing features with random values for that feature in the training set.

Fill in missing features with the average of observed values for that feature in the entire dataset.

Delete entire columns that contain any missing features.

18. 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

19. Which of the following unsupervised learning models can a bank use for fraud detection?

Anomaly detection

DB5CAN

Hierarchical clustering

k-means

20. 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?

Normalization

Oversampling

Principal components analysis

Quality filtering

Page 3 of 3

21. Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?

Asset

Code Repository

Configuration Management

Incident

22. 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.

23. Which two of the following criteria are essential for machine learning models to achieve before deployment? (Select two.)

Complexity

Data size

Explainability

Portability

Scalability

24. The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.

What would be an optimal value of K to ensure a good number of clusters?

25. Which of the following best describes distributed artificial intelligence?

It does not require hyperparemeter tuning because the distributed nature accounts for the bias.

It intelligently pre-distributes the weight of starting a neural network.

It relies on a distributed system that performs robust computations across a network of unreliable nodes.

It uses a centralized system to speak to decentralized nodes.

26. 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

Explore the data for outliers.

Subtract the mean of Y from all the Y values.

27. Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?

As new resources become available

Once a month

When the input format changes

When the input volume changes

[email protected]

Mon - Sat 9:00am - 6:00pm

Use AIP-210 Dumps to Obtain CertNexus Certification

Related

Posts

About Us

EMAIL

Services

Opening Hours