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NEW QUESTION 51
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, and then deploy the model on AI Platform Prediction.
- C. 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.
- D. Embed the client on the website, and then deploy the model on AI Platform Prediction.
Answer: B
NEW QUESTION 52
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
- B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
Answer: A
Explanation:
https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/
NEW QUESTION 53
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- B. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- D. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
Answer: D
Explanation:
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture
NEW QUESTION 54
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
- A. Store your tf.logging data in BigQuery.
- B. Manage your ML workflows with Vertex ML Metadata.
- C. Store all ML metadata in Google Cloud’s operations suite.
- D. Manage all relational entities in the Hive Metastore.
Answer: C
NEW QUESTION 55
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+2 = 161154
- B. 500*256*0 25+256*128*0 25+128*2 = 40448
- C. 500*256+256*128+128*2 = 161024
- D. 501*256+257*128+128*2=161408
Answer: D
NEW QUESTION 56
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