Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A.1. Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• B.1. Create a Pub/Sub topic for each user
  2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• C.1 Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• D.1. Create a Pub/Sub topic for each user
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

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A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

• A.Convert the records to Apache Parquet format.
• B.Convert the records to JSON format.
• C.Convert the records to GZIP CSV format.
• D.Convert the records to XML format.

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Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

• A.Perform feature selection on the model, and retrain the model with fewer features
• B.Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
• C.Create alerts to monitor for skew, and retrain the model.
• D.Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

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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.501*256+257*128+128*2=161408
• C.500*256*0 25+256*128*0 25+128*2 = 40448
• D.500*256+256*128+128*2 = 161024

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A Machine Learning Specialist wants to determine the appropriate
SageMakerVariantInvocationsPerInstancesetting for an endpoint automatic scaling configuration.
The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.
Based on the stated parameters and given that the invocations per instance setting is measured on a per- minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?

• A.10
• B.2,400
• C.30
• D.600

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