QUESTION: 161

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?

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.
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.
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.
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.

Answer(s): C

Reference:

https://docs.aws.amazon.com/lex/latest/dg/lex-dg.pdf

Reveal Solution Next Question

QUESTION: 162

A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.

The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?

Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.
Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Answer(s): C

Reveal Solution Next Question

QUESTION: 163

A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

Apply integer transformation and set Red = 1, White = 5, and Green = 10.
Add new columns that store one-hot representation of colors.
Replace the color name string by its length.
Create three columns to encode the color in RGB format.
Replace each color name by its training set frequency.

Answer(s): B,E

Reveal Solution Next Question

QUESTION: 164

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer(s): A

Explanation:

The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

In this scenario, where the traffic behavior at each light is correlated, a multi-agent reinforcement learning (MARL) approach is well-suited to model the problem. In MARL, multiple agents interact with each other and the environment, and their behavior is influenced by the behavior of other agents. This approach is particularly useful in modeling traffic systems, where the behavior of each vehicle is affected by the behavior of other vehicles and traffic lights.

Formulating the problem as a MARL problem can help the data scientist obtain a correlated equilibrium policy, which can optimize traffic flow across multiple traffic lights by taking into account the correlations between them. By optimizing traffic flow across all traffic lights in a correlated way, it may be possible to reduce congestion and improve overall traffic efficiency.

Reference:

https://www.researchgate.net/publication/221456376_Multi-Agent_Reinforcement_Learning_for_Simulating_Pedestrian_Navigation

Reveal Solution Next Question

Free MLS-C01 Exam Braindumps (page: 41)

QUESTION: 161

Reference:

QUESTION: 162

QUESTION: 163

QUESTION: 164

Explanation:

Reference: