Amazon MLS-C01 Exam Dumps

A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

A. Download the AWS SDK for the Spark environment

B. Install the SageMaker Spark library in the Spark environment.

C. Use the appropriate estimator from the SageMaker Spark Library to train a model.

D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

E. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

F. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an,” and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.

What should the data scientist do to meet these requirements?

A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

C. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.

D. Remove the stopwords from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

An online advertising company is developing a linear model to predict the bid price of advertisements in real time with low-latency predictions. A data scientist has trained the linear model by using many features, but the model is overfitting the training dataset. The data scientist needs to prevent overfitting and must reduce the number of features.

Which solution will meet these requirements?

A. Retrain the model with L1 regularization applied.

B. Retrain the model with L2 regularization applied.

C. Retrain the model with dropout regularization applied.

D. Retrain the model by using more data.