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

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

1. A. Use an AWS Lambda function to process the dat
2. B. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
3. C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalo
4. D. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.
5. E. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalo
6. F. Use the FindMatches transform to cleanse the data.
7. G. Create an AWS Lake Formation custom transfor
8. H. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Correct Answer: D

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

1. A. Latent Dirichlet Allocation (LDA)
2. B. Recurrent neural network (RNN)
3. C. K-means
4. D. Convolutional neural network (CNN)

Correct Answer: D

A data science team is planning to build a natural language processing (NLP) application. The application’s text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.
Which solution can the team build MOST quickly to meet these requirements?

1. A. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
2. B. Use an NLP library in Amazon SageMaker for the part-of-speech taggin
3. C. Use Amazon Comprehend for the key phase extractio
4. D. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
5. E. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction task
6. F. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
7. G. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction task
8. H. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

Correct Answer: B

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model
What should the Specialist do to address the performance issues with the current solution?

1. A. Use the SageMaker batch transform feature
2. B. Compress the training data into Apache Parquet format.
3. C. Ensure that the input mode for the training job is set to Pipe.
4. D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Correct Answer: B