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NEW QUESTION 22
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

• A. 1 = Pub/Sub, 2 = Datastore
• B. 1 = Cloud Function, 2 = Cloud SQL
• C. 1 = Dataflow, 2 = Cloud SQL
• D. 1 = Dataflow, 2 = BigQuery

Answer: A

NEW QUESTION 23
You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

• A. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
• B. Use latitude, longitude, and product type as features. Use profit as model output.
• C. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
• D. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.

Answer: C

NEW QUESTION 24
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• B. Redeploy the model on a P3dn instance.
• C. Redeploy the model as a batch transform job on an M5 instance.
• D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

NEW QUESTION 25
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

• A. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
• B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
• C. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
• D. Embed the client on the website, and then deploy the model on AI Platform Prediction.

Answer: A

Explanation:
https://medium.com/google-cloud/secure-cloud-run-cloud-functions-and-app-engine-with-api-key-73c57bededd1

NEW QUESTION 26
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