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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q328-Q333):
NEW QUESTION # 328
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)
- A. Emails exchanged by customers and the company's customer service agents
- B. Product sales revenue figures for the company
- C. A publicly available collection of news articles
- D. A publicly available collection of customer reviews
- E. Instruction manuals for the company's products
- F. Social media posts containing the name of the company or its products
Answer: A,D,F
Explanation:
The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company's customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers. Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.
References:
Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog How to Apply Machine Learning to Customer Feedback
NEW QUESTION # 329
A 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 orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar 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 Cartesian product transformation to create new combinations of fields that are independent of the magnitude
Answer: C
NEW QUESTION # 330
A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.
Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product." The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.
Which functionality of Amazon Comprehend should the team use to meet these requirements?
- A. Custom classification with multi-label mode
- B. Built-in models
- C. Custom entity recognition
- D. Custom classification with multi-class mode
Answer: A
Explanation:
The problem involves assigning multiple related, non-mutually exclusive categories (like "yogurt", "snack", and "dairy product") to customer comments referencing specific products. Amazon Comprehend provides custom classification capabilities, and in this case, the appropriate mode is multi-label, not multi-class.
"Use multi-label mode when each document can belong to more than one class. This is appropriate for situations where the categories are not mutually exclusive." This allows a single document to be tagged with several relevant labels simultaneously, which is precisely the use case described.
NEW QUESTION # 331
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 the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
- B. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
- C. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
- D. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html
NEW QUESTION # 332
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis What is the MOST efficient way to accomplish these tasks'?
- A. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data
- B. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
- C. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
- D. Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
Answer: B
NEW QUESTION # 333
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