Online Access Free Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-11-29.q177 Practice Test (Page 2)

AWS-Certified-Machine-Learning-Specialty Exam Question 1

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?

A.Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

B.Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

C.Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

D.Reduce the learning rate and run the training process until the training loss stops decreasing.

AWS-Certified-Machine-Learning-Specialty Exam Question 2

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

A.Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

B.Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

C.Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

D.Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

E.Use Amazon Rekognition Custom Labels to train a new model.

F.Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

AWS-Certified-Machine-Learning-Specialty Exam Question 3

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A.Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C.Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

D.Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

AWS-Certified-Machine-Learning-Specialty Exam Question 4

Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A.Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.

B.Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

C.Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.

D.Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

AWS-Certified-Machine-Learning-Specialty Exam Question 5

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

A.Create one-hot word encoding vectors.

B.Produce a set of synonyms for every word using Amazon Mechanical Turk.

C.Create word embedding vectors that store edit distance with every other word.

D.Download word embeddings pre-trained on a large corpus.

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AWS-Certified-Machine-Learning-Specialty Exam Question 1

AWS-Certified-Machine-Learning-Specialty Exam Question 2

AWS-Certified-Machine-Learning-Specialty Exam Question 3

AWS-Certified-Machine-Learning-Specialty Exam Question 4

AWS-Certified-Machine-Learning-Specialty Exam Question 5

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