A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?

1. A. Decrease the cooldown period for the scale-in activit
2. B. Increase the configured maximum capacity of instances.
3. C. Replace the current endpoint with a multi-model endpoint using SageMaker.
4. D. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
5. E. Increase the cooldown period for the scale-out activity.

Correct Answer: A

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model
What should the Specialist do to address the performance issues with the current solution?

1. A. Use the SageMaker batch transform feature
2. B. Compress the training data into Apache Parquet format.
3. C. Ensure that the input mode for the training job is set to Pipe.
4. D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Correct Answer: B

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy
Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

1. A. Launch multiple training jobs in parallel with different hyperparameters
2. B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
3. C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
4. D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Correct Answer: B

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

1. A. The model predicts both the trend and the seasonality well.
2. B. The model predicts the trend well, but not the seasonality.
3. C. The model predicts the seasonality well, but not the trend.
4. D. The model does not predict the trend or the seasonality well.

Correct Answer: D

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)

1. A. Add L1 regularization to the classifier
2. B. Add features to the dataset
3. C. Perform recursive feature elimination
4. D. Perform t-distributed stochastic neighbor embedding (t-SNE)
5. E. Perform linear discriminant analysis

Correct Answer: BE