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

A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company’s data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model’s testing accuracy.
Which process will improve the testing accuracy the MOST?

1. A. Use a one-hot encoder for the categorical fields in the datase
2. B. Perform standardization on the financial fields in the datase
3. C. Apply L1 regularization to the data.
4. D. Use tokenization of the categorical fields in the datase
5. E. Perform binning on the financial fields in the datase
6. F. Remove the outliers in the data by using the z-score.
7. G. Use a label encoder for the categorical fields in the datase
8. H. Perform L1 regularization on the financial fields in the datase
9. I. Apply L2 regularization to the data.
10. J. Use a logarithm transformation on the categorical fields in the datase
11. K. Perform binning on the financial fields in the datase
12. L. Use imputation to populate missing values in the dataset.

Correct Answer: B

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

1. A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in thecustomer database.
2. B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
3. C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
4. D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

Correct Answer: C

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?

1. A. An XGBoost model where the objective parameter is set to multi: softmax
2. B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
3. C. A regression forest where the number of trees is set equal to the number of product categories
4. D. A DeepAR forecasting model based on a recurrent neural network (RNN)

Correct Answer: A

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

1. A. The model predicts both the trend and the seasonality well.
2. B. The model predicts the trend well, but not the seasonality.
3. C. The model predicts the seasonality well, but not the trend.
4. D. The model does not predict the trend or the seasonality well.

Correct Answer: D

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

1. A. Change preprocessing to use n-grams.
2. B. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
3. C. Adjust hyperparameters related to the attention mechanism.
4. D. Choose a different weight initialization type.

Correct Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq-howitworks.html