QUESTION: 157

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
Use Amazon Rekognition Custom Labels to train a new model.
Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

Answer(s): C,D,F

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QUESTION: 158

A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B, 240 samples for category C, 258 samples for category D, and 310 samples for category E.
The data scientist shuffiles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets.

What could the data scientist conclude form these results?

Classes C and D are too similar.
The dataset is too small for holdout cross-validation.
The data distribution is skewed.
The model is overfitting for classes B and E.

Answer(s): A

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QUESTION: 159

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

Data scaling with standardization and normalization
Correlation plot with heat maps
Data binning
Univariate selection
Feature importance with a tree-based classifier
Data augmentation

Answer(s): B,D,E

Explanation:

B) Correlation plot with heat maps: This technique can be used to identify the relationship between each feature and the target variable (sales price). By creating a correlation plot with heat maps, the company can quickly visualize the strength and direction of the relationship between each feature and the target variable.

D) Univariate selection: This technique can be used to select the features that have the strongest relationship with the target variable. It involves analyzing each feature independently and selecting the ones that have the highest correlation with the target variable.

E) Feature importance with a tree-based classifier: This technique can be used to determine the most important features that contribute to the target variable. By using a tree-based classifier such as Random Forest or Gradient Boosting, the company can rank the importance of each feature and select the ones that have the highest importance.

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QUESTION: 160

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.

The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

Autoregressive Integrated Moving Average (AIRMA)
Exponential Smoothing (ETS)
Convolutional Neural Network - Quantile Regression (CNN-QR)
Prophet

Answer(s): C

Reference:

https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-choosing-recipes.html

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QUESTION: 157

QUESTION: 158

QUESTION: 159

Explanation:

QUESTION: 160

Reference: