Free AWS-Certified-Machine-Learning-Specialty Exam Braindumps

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 data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

1. A. Shuffle all interaction dat
2. B. Split off the last 10% of the interaction data for the test set.
3. C. Identify the most recent 10% of interactions for each use
4. D. Split off these interactions for the test set.
5. E. Identify the 10% of users with the least interaction dat
6. F. Split off all interaction data from these users for the test set.
7. G. Randomly select 10% of the user
8. H. Split off all interaction data from these users for the test set.

Correct Answer: B
https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagem

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available
Which model is MOST likely to provide the best results in Amazon SageMaker?

1. A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
2. B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
3. C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of regressor.
4. D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of classifier.

Correct Answer: C

A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.
Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

1. A. Amazon SageMaker seq2seq algorithm
2. B. Amazon SageMaker BlazingText algorithm in Skip-gram mode
3. C. Amazon SageMaker Object2Vec algorithm
4. D. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
5. E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

Correct Answer: AC

A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users’ interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

1. A. Load the data into an Amazon Redshift cluste
2. B. Execute the pipeline by using SQ
3. C. Store the results in Amazon S3.
4. D. Load the data into Amazon DynamoD
5. E. Convert the scripts to an AWS Lambda functio
6. F. Execute the pipeline by triggering Lambda execution
7. G. Store the results in Amazon S3.
8. H. Create an AWS Glue jo
9. I. Convert the scripts to PySpar
10. J. Execute the pipelin
11. K. Store the results in Amazon S3.
12. L. Create a set of individual AWS Lambda functions to execute each of the script
13. M. Build a step function by using the AWS Step Functions Data Science SD
14. N. Store the results in Amazon S3.

Correct Answer: B