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Amazon MLS-C01 Certification Exam is ideal for individuals looking to build a career in machine learning on AWS. AWS Certified Machine Learning - Specialty certification is recognized globally, and it demonstrates an individual's ability to implement and maintain scalable and reliable ML solutions on AWS platform. AWS Certified Machine Learning - Specialty certification is also highly valued by organizations that are looking to hire ML professionals, as it demonstrates a high level of expertise in machine learning on AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q227-Q232):

NEW QUESTION # 227
An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

A. Install AWS DeepLens cameras and use the DeepLens_Kinesis_video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.
B. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected
C. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video strearw On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.

Answer: D

NEW QUESTION # 228
A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

A. Linear regression
B. Principal component analysis (PCA)
C. Logistic regression
D. Random Cut Forest (RCF)

Answer: A

NEW QUESTION # 229
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

A. Add the unrecognized words as synonyms in the custom slot type.
B. Add the unrecognized words in the enumeration values list as new values in the slot type.
C. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
D. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.

Answer: D

NEW QUESTION # 230
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE)

A. Alexa for Business
B. Amazon Connect
C. Amazon Transcribe
D. Amazon Poly
E. Amazon Comprehend
F. Amazon Lex

Answer: C,E,F

Explanation:
To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user's intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:
Amazon Lex: A service for building conversational interfaces into any application using voice and text.
Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user's requests.
Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics.
Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.
Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.
Therefore, the company can use the following architecture to build the conversational interface:
Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user's query and parameters, such as the report name, date, metric, or filter.
Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex.
Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.
Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user's query, and the corresponding data from the reports.
Use a lambda function to implement the business logic and fulfillment of the user's query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.
Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user's preference.
What Is Amazon Lex?
What Is Amazon Comprehend?
What Is Amazon Transcribe?

NEW QUESTION # 231
For the given confusion matrix, what is the recall and precision of the model?

A. Recall = 0.84 Precision = 0.8
B. Recall = 0.92 Precision = 0.8
C. Recall = 0.92 Precision = 0.84
D. Recall = 0.8 Precision = 0.92

Answer: B

Explanation:
Recall and precision are two metrics that can be used to evaluate the performance of a classification model.
Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:
Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8
Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

NEW QUESTION # 232
......

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