A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.Which approach will provide the MAXIMUM performance boost?
Answer(s): D
Word2vec is a technique that can learn distributed representations of words, also known as word embeddings, from large amounts of text data. Word embeddings can capture the semantic and syntactic similarities and relationships between words, and can be used as input features for neural network models. Word2vec can be trained on domain-specific corpora to obtain more relevant and accurate word embeddings for a particular task.
A Machine Learning Specialist needs to move and transform data in preparation for training. Some of the data needs to be processed in near-real time, and other data can be moved hourly. There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.Which of the following services can feed data to the MapReduce jobs? (Choose two.)
Answer(s): B,C
https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
Answer(s): A
For SageMaker to run a container for training or hosting, it needs to be able to find the image hosted in the image repository, Amazon Elastic Container Registry (Amazon ECR). The three main steps to this process are building locally, tagging with the repository location, and pushing the image to the repository.
https://sagemaker-workshop.com/custom/containers.html
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.Which storage option provides the most processing flexibility and will allow access control with IAM?
Answer(s): C
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.Which feature engineering technique should the Data Scientist use to meet the objectives?
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