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NEW QUESTION 24
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

• A. Decrease the recall.
• B. Increase the number of false positives
• C. Increase the recall
• D. Decrease the number of false negatives

Answer: D

NEW QUESTION 25
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

• A. Use an established text classification model on Al Platform to perform transfer learning
• B. Use AutoML Natural Language to build the support requests classifier
• C. Use an established text classification model on Al Platform as-is to classify support requests
• D. Use the Natural Language API to classify support requests

Answer: C

NEW QUESTION 26
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

• A. Drop the age feature from the dataset and train the model using the rest of the features.
• B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
• C. Use k-means clustering to handle missing features
• D. Drop all records from the dataset where age has been set to 0.

Answer: D

Explanation:
Explanation

NEW QUESTION 27
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