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Complete MLS-C01 Amazon Web Services Materials

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Total 330 questions

AWS Certified Machine Learning - Specialty Questions and Answers

Question 65

A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

Options:

A.

Use an Amazon SageMaker notebook for both feature engineering and model development

B.

Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

C.

Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

D.

Use Amazon ML for both feature engineering and model development.

Question 66

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents?

Options:

A.

Convert current documents to SSML with pronunciation tags

B.

Create an appropriate pronunciation lexicon.

C.

Output speech marks to guide in pronunciation

D.

Use Amazon Lex to preprocess the text files for pronunciation

Question 67

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

Options:

A.

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

B.

Use AWS Glue to catalogue the data and Amazon Athena to run queries

C.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

D.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Question 68

A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.

The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.

Which approach will maximize transcription accuracy during the development phase?

Options:

A.

Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.

B.

Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.

C.

Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. 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.

D.

Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.

Page: 17 / 24
Total 330 questions