Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

 The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.

Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.

Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

Amazon Transcribe – Custom Vocabulary

Amazon Transcribe – Custom Language Models

[Amazon Lex – Limits]

The correct combination of actions to enable the data scientist’s IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:

B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.

C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.

E: Including the SQL statement “USING EXTERNAL FUNCTION ml_function_name” in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.

The other options are not correct or necessary, because they have the following drawbacks:

A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.

D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.

F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.

1: InvokeEndpoint - Amazon SageMaker

2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena

3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog

4: AmazonAthenaFullAccess - AWS Identity and Access Management

5: GetRecord - Amazon SageMaker Feature Store Runtime

[Invoke a Multi-Model Endpoint - Amazon SageMaker]

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.

Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog

How to Apply Machine Learning to Customer Feedback