AWS-Certified-Machine-Learning-Specialty Free Practice Test

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models
During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images
Which of the following should be used to resolve this issue? (Select TWO)

1. A. Add vanishing gradient to the model
2. B. Perform data augmentation on the training data
3. C. Make the neural network architecture complex.
4. D. Use gradient checking in the model
5. E. Add L2 regularization to the model

Correct Answer: BD

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

1. A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea
2. B. On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
3. C. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea
4. D. On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.
5. E. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer
6. F. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.
7. G. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer
8. H. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

Correct Answer: C

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

1. A. The learning rate should be increased because the optimization process was trapped at a local minimum.
2. B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
3. C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
4. D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Correct Answer: A

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

1. A. Increase the training data by adding variation in rotation for training images.
2. B. Increase the number of epochs for model training.
3. C. Increase the number of layers for the neural network.
4. D. Increase the dropout rate for the second-to-last layer.

Correct Answer: A

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

1. A. Detecting seasonality for the majority of stores will be an issu
2. B. Request categorical data to relate new stores with similar stores that have more historical data.
3. C. The sales data does not have enough varianc
4. D. Request external sales data from other industries to improve the model's ability to generalize.
5. E. Sales data is aggregated by wee
6. F. Request daily sales data from the source database to enable building a daily model.
7. G. The sales data is missing zero entries for item sale
8. H. Request that item sales data from the source database include zero entries to enable building the model.
9. I. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Correct Answer: AB