Question 1

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

Options :

A : Add synthetic training data where those phrases are used in non-toxic ways.

B : Remove the model and replace it with human moderation.

C : Replace your model with a different text classifier.

D : Raise the threshold for comments to be considered toxic or harmful.

Answer: A

Question 2

You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

Options :

A : Use Vertex Al manual split, using the store name feature to assign one store for each set.

B : Use Vertex Al default data split.

C : Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.

D :

Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.

Answer: B

Question 3

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

Options :

A : Train local surrogate models to explain individual predictions.

B : Configure sampled Shapley explanations on Vertex Explainable AI.

C : Configure integrated gradients explanations on Vertex Explainable AI.

D : Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: B

Question 4

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

What is the correct way to write the predict request?

Options :

A : data = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})

B : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})

C : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})

D : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Answer: D

Question 5

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

Options :

A : Use Principal Component Analysis to eliminate the least informative features.

B : Use L1 regularization to reduce the coefficients of uninformative features to 0.

C : After building your model, use Shapley values to determine which features are the most informative.

D : Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer: B