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

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

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training
What should the Specialist do to optimize the data for training on SageMaker'?

1. A. Use the SageMaker batch transform feature to transform the training data into a DataFrame
2. B. Use AWS Glue to compress the data into the Apache Parquet format
3. C. Transform the dataset into the Recordio protobuf format
4. D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

Correct Answer: C

An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

1. A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
2. B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
3. C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
4. D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Correct Answer: C

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset
Which tool should be used to improve the validation accuracy?

1. A. Amazon Comprehend syntax analysts and entity detection
2. B. Amazon SageMaker BlazingText allow mode
3. C. Natural Language Toolkit (NLTK) stemming and stop word removal
4. D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Correct Answer: A

A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

1. A. Exponential transformation
2. B. Logarithmic transformation
3. C. Polynomial transformation
4. D. Sinusoidal transformation

Correct Answer: A