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NEW QUESTION 22
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
- A. Decrease dropout.
- B. Increase dropout.
- C. Increase regularization.
- D. Decrease regularization.
- E. Decrease feature combinations.
- F. Increase feature combinations.
NEW QUESTION 23
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?
- A. Output speech marks to guide in pronunciation.
- B. Convert current documents to SSML with pronunciation tags.
- C. Use Amazon Lex to preprocess the text files for pronunciation
- D. Create an appropriate pronunciation lexicon.
NEW QUESTION 24
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?
- A. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
- C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
- D. Write a direct connection to the SQL database within the notebook and pull data in
NEW QUESTION 25
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?
- A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- B. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
- C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
- D. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
NEW QUESTION 26
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. A DeepAR forecasting model based on a recurrent neural network (RNN)
- B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- C. An XGBoost model where the objective parameter is set to multi: softmax
- D. A regression forest where the number of trees is set equal to the number of product categories
NEW QUESTION 27
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