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NEW QUESTION 52
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
- A. 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API
- B. 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API
- C. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
- D. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.
NEW QUESTION 53
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company’s dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model’s complexity?
- A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
- B. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
- C. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
- D. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
NEW QUESTION 54
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn’t changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
- A. Too few layers in the model for capturing information
- B. Incorrect data split ratio during model training, evaluation, validation, and test
- C. Poor data quality
- D. Lack of model retraining
NEW QUESTION 55
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- C. Significantly increase the max_batch_size TensorFlow Serving parameter
- D. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
NEW QUESTION 56
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