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Question 236

An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

A.The Latent Dirichlet Allocation (LDA) algorithm
B.The principal component analysis (PCA) algorithm
C.The factorization machines (FM) algorithm
D.The k-means algorithm g The Random Cut Forest (RCF) algorithm

Question 237

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?

A.Train a new model using the current neural network architecture.
B.Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C.Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
D.Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Question 238

The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data Which machine learning algorithm should the researchers use that BEST meets their requirements?

A.Latent Dirichlet Allocation (LDA)
B.Recurrent neural network (RNN)
C.K-means
D.Convolutional neural network (CNN)

Correct Answer: D

A convolutional neural network (CNN) is a type of machine learning algorithm that is suitable for image classification tasks. A CNN consists of multiple layers that can extract features from images and learn to recognize patterns and objects. A CNN can also use transfer learning to leverage pre-trained models that have been trained on large-scale image datasets, such as ImageNet, and fine-tune them for specific tasks, such as detecting the company's retail brand. A CNN can achieve high accuracy and performance for image classification problems, as it can handle complex and diverse images and reduce the dimensionality and noise of the input data. A CNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc12 The other options are not valid or relevant for the image classification task. Latent Dirichlet Allocation (LDA) is a type of machine learning algorithm that is suitable for topic modeling tasks. LDA can discover the hidden topics and their proportions in a collection of text documents, such as news articles, tweets, reviews, etc. LDA is not applicable for image data, as it requires textual input and output. LDA can be implemented using various frameworks and libraries, such as Gensim, Scikit-learn, Mallet, etc34 Recurrent neural network (RNN) is a type of machine learning algorithm that is suitable for sequential data tasks. RNN can process and generate data that has temporal or sequential dependencies, such as natural language, speech, audio, video, etc. RNN is not optimal for image data, as it does not capture the spatial features and relationships of the pixels. RNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc.
K-means is a type of machine learning algorithm that is suitable for clustering tasks. K-means can partition a set of data points into a predefined number of clusters, based on the similarity and distance between the data points. K-means is not suitable for image classification tasks, as it does not learn to label the images or detect the objects of interest. K-means can be implemented using various frameworks and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc.

Question 239

A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

A.Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
B.Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
C.Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
D.Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

Question 240

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

A.Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
B.Run a SageMaker training job with an EFS file system as the data source.
C.Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
D.Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.