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AWS Certified Machine Learning - Specialty Questions and Answers

Question 69

A machine learning specialist works for a fruit processing company and needs to build a system that

categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.

The company requires at least 85% accuracy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68% accuracy on the training set

67% accuracy on the validation set

What can the machine learning specialist do to improve the system’s accuracy?

Options:

Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model’s hyperparameters.

Add more data to the training set and retrain the model using transfer learning to reduce the bias.

Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Train a new model using the current neural network architecture.

Question 70

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.

Which visualization will help the data scientist better understand the data trend?

Options:

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

Question 71

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.)

Options:

Emails exchanged by customers and the company’s customer service agents

Social media posts containing the name of the company or its products

A publicly available collection of news articles

A publicly available collection of customer reviews

Product sales revenue figures for the company

Instruction manuals for the company’s products

Question 72

An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.

What combination of services is the MOST efficient to accomplish the task?

Options:

Amazon Transcribe, Amazon Translate, and Amazon Comprehend

Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

Answer:

Explanation:

Amazon Transcribe, Amazon Translate, and Amazon Comprehend are the most efficient combination of services to accomplish the task of sentiment analysis on a video clip with audio in Spanish. Amazon Transcribe is a service that can convert speech to text using deep learning. Amazon Transcribe can transcribe audio from various sources, such as video files, audio files, or streaming audio. Amazon Transcribe can also recognize multiple speakers, different languages, accents, dialects, and custom vocabularies. In this case, Amazon Transcribe can transcribe the audio from the video clip in Spanish to text in Spanish1 Amazon Translate is a service that can translate text from one language to another using neural machine translation. Amazon Translate can translate text from various sources, such as documents, web pages, chat messages, etc. Amazon Translate can also support multiple languages, domains, and styles. In this case, Amazon Translate can translate the text from Spanish to English2 Amazon Comprehend is a service that can analyze and derive insights from text using natural language processing. Amazon Comprehend can perform various tasks, such as sentiment analysis, entity recognition, key phrase extraction, topic modeling, etc. Amazon Comprehend can also support multiple languages and domains. In this case, Amazon Comprehend can perform sentiment analysis on the text in English and determine whether the feedback is positive, negative, neutral, or mixed3

The other options are not valid or efficient for accomplishing the task of sentiment analysis on a video clip with audio in Spanish. Amazon Comprehend, Amazon SageMaker seq2seq, and Amazon SageMaker Neural Topic Model (NTM) are not a good combination, as they do not include a service that can transcribe speech to text, which is a necessary step for processing the audio from the video clip. Amazon Comprehend, Amazon Translate, and Amazon SageMaker BlazingText are not a good combination, as they do not include a service that can perform sentiment analysis, which is the main goal of the task. Amazon SageMaker BlazingText is a service that can train and deploy text classification and word embedding models using deep learning. Amazon SageMaker BlazingText can perform tasks such as text classification, named entity recognition, part-of-speech tagging, etc., but not sentiment analysis4

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AWS Certified Machine Learning - Specialty Questions and Answers

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