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NEW QUESTION 46
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

  • A. AWS Secrets Manager
  • B. Amazon ECS
  • C. AWS CodeStar
  • D. Amazon S3
  • E. Amazon ECR

Answer: D,E

 

NEW QUESTION 47
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

  • A. Set the early stopping parameter to TRUE
  • B. Decrease the maximum number of trials during subsequent training phases.
  • C. Decrease the range of floating-point values
  • D. Change the search algorithm from Bayesian search to random search.
  • E. Decrease the number of parallel trials

Answer: B,D

 

NEW QUESTION 48
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

  • A. This is a good result because the accuracy across both groups is greater than 80%.
  • B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
  • C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
  • D. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

Answer: C

 

NEW QUESTION 49
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model’s predictions will be used to adapt each user’s game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

  • A. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
  • B. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
  • C. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
  • D. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Answer: A

 

NEW QUESTION 50
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

  • A. Use a time series forecasting model to predict each item’s monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
  • B. Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
  • C. Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.
  • D. Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.

Answer: D

 

NEW QUESTION 51
……

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