AWS Certified Machine Learning Specialty (MLS-C01)

The AWS Certified Machine Learning Specialty (MLS-C01) were last updated on today.
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Topic 1 - Exam A

Question #1 Topic 1

A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies. Which model should be used for categorizing new products using the provided dataset for training?

  • A AnXGBoost model where the objective parameter is set to multi:softmax
  • B A deep convolutional neural network (CNN) with a softmax activation function for the last layer
  • C A regression forest where the number of trees is set equal to the number of product categories
  • D A DeepAR forecasting model based on a recurrent neural network (RNN)
Suggested Answer: B
NOTE:
Question #2 Topic 1

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models. What should the Specialist do to initialize the model to re-train it with the custom data?

  • A Initialize the model with random weights in all layers including the last fully connected layer.
  • B Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
  • C Initialize the model with random weights in all layers and replace the last fully connected layer.
  • D Initialize the model with pre-trained weights in all layers including the last fully connected layer.
Suggested Answer: B
NOTE:
Question #3 Topic 1

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily. The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes. What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  • A Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  • B Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
  • C Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  • D Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
Suggested Answer: B
NOTE:
Question #4 Topic 1

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates. What is the MOST likely cause of this issue?

  • A The class distribution in the dataset is imbalanced.
  • B Dataset shuffling is disabled.
  • C The batch size is too big.
  • D The learning rate is very high.
Suggested Answer: D
NOTE: Reference: https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95
Question #5 Topic 1

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment. Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

  • A Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
  • B Use SCPs to restrict access to SageMaker.
  • C Disable root access on the SageMaker notebook instances.
  • D Enable network isolation for training jobs and models.
  • E Restrict notebook presigned URLs to specific IPs used by the company.
  • F Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.
Suggested Answer: BDF
NOTE: