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Some new sample questions:
Question:
A company decides to use Amazon SageMaker to develop machine learning (ML) models. The company will host SageMaker notebook instances in a VPC. The company stores training data in an Amazon S3 bucket. Company security policy states that SageMaker notebook instances must not have internet connectivity.
Which solution will meet the company’s security requirements?
A. Connect the SageMaker notebook instances that are in the VPC by using AWS Site-to-Site VPN to encrypt all internet-bound traffic. Configure VPC flow logs. Monitor all network traffic to detect and prevent any malicious activity.
B. Configure the VPC that contains the SageMaker notebook instances to use VPC interface endpoints to establish connections for training and hosting. Modify any existing security groups that are associated with the VPC interface endpoint to only allow outbound connections for training and hosting.
C. Create an IAM policy that prevents access to the internet. Apply the IAM policy to an IAM role. Assign the IAM role to the SageMaker notebook instances in addition to any IAM roles that are already assigned to the instances.
D. Create VPC security groups to prevent all incoming and outgoing traffic. Assign the security groups to the SageMaker notebook instances.
Question:
A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system. The company is using Amazon SageMaker hosting services to develop a recommendation model in a single Availability Zone within an AWS Region.
A machine learning (ML) specialist must develop a solution to achieve high availability. The solution must have a recovery time objective (RTO) of 5 minutes.
Which solution will meet these requirements with the LEAST effort?
A. Deploy multiple instances for each endpoint in a VPC that spans at least two Regions.
B. Use the SageMaker auto scaling feature for the hosted recommendation models.
C. Deploy multiple instances for each production endpoint in a VPC that spans at least two subnets that are in a second Availability Zone.
D. Frequently generate backups of the production recommendation model. Deploy the backups in a second Region.
Question:
A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package.
During model training, the data scientist needs to evaluate model performance.
Which metrics should the data scientist use to meet this requirement? (Select TWO.)
A. InferenceLatency
B. Mean squared error (MSE)
C. Root mean squared error (RMSE)
D. Precision
E. Accuracy
…..
Some new quesstions:
Q
An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection – TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.
Which approach should the ML specialist use to improve the performance of the model on the testing data?
A. Increase the value of the momentum hyperparameter.
B. Reduce the value of the dropout_rate hyperparameter.
C. Reduce the value of the learning_rate hyperparameter.
D. Increase the value of the L2 hyperparameter.
Q
A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company’s stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?
A. Aggregate sales from stores in the same geographic area.
B. Apply smoothing to correct for seasonal variation.
C. Change the forecast frequency from daily to weekly.
D. Replace missing values in the dataset by using linear interpolation.
Q
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.
After training, the model’s inferences accuracy is lower than expected.
Which preprocessing technique will result in the GREATEST increase of the model’s inference accuracy?
A. Normalize the problematic features.
B. Bootstrap the problematic features.
C. Remove the problematic features.
D. Extrapolate synthetic features.
Q
A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model’s accuracy?
A. Adjust the class weight to account for each machine type.
B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
C. Undersample the non-failure events. Stratify the non-failure events by machine type.
D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).
………..