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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions:

1. A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

A) Set up SageMaker Debugger and create a custom rule.
B) Set up blue/green deployments with all-at-once traffic shifting.
C) Set up blue/green deployments with canary traffic shifting.
D) Set up shadow testing with a shadow variant of the new model.

2. A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

A) Use Amazon Mechanical Turk jobs to detect duplicates.
B) Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
C) Use the AWS Glue FindMatches transform to detect duplicates.
D) Use Amazon QuickSight ML Insights to build a custom deduplication model.

3. An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

4. An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

A) Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
B) Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
C) Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
D) Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

5. An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

A) Increase the learning rate.
B) Introduce early stopping.
C) Increase the size of the test set.
D) Decrease the learning rate.

Solutions: