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NEW QUESTION: 1
Which three of the following are recommended steps for the design phase? (Choose three.)
A. High-level design
B. Proof of concept
C. Competitive market analysis
D. Gap analysis
E. Low-level design
Answer: A,B,E

NEW QUESTION: 2
A company recently started using Amazon Aurora as the data store for its global ecommerce application. When large reports are run, developers report that the ecommerce application is performing poorly. After reviewing metrics in Amazon CloudWatch, a solutions architect finds that the ReadlOPS and CPU Utilization metrics are spiking when monthly reports run.
What is the MOST cost-effective solution?
A. Migrate the monthly reporting to an Aurora Replica.
B. Increase the Provisioned IOPS on the Aurora instance.
C. Migrate the Aurora database to a larger instance class.
D. Migrate the monthly reporting to Amazon Redshift.
Answer: B

NEW QUESTION: 3
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Number of hidden layers
B. Number of nodes in each hidden layer
C. Weights
D. Biases
Answer: A,B
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview