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Question 221

A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.
Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.
Which solution will meet these requirements?

A.Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.
B.Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.
C.Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.
D.Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances.Implement model checkpoints.

Correct Answer: C

Explanation
The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.
The other options are not suitable because:
Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance.
Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.
Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.
Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.
References:
1: Managed Spot Training - Amazon SageMaker
2: Pipe Mode - Amazon SageMaker
3: FastFile Mode - Amazon SageMaker
4: Checkpoints - Amazon SageMaker

Question 222

A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products.
The labeled dataset has 15 features for each product such as title dimensions, weight, and price.
Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A.AnXGBoost model where the objective parameter is set to multi:softmax
B.A deep convolutional neural network (CNN) with a softmax activation function for the last layer
C.A regression forest where the number of trees is set equal to the number of product categories
D.A DeepAR forecasting model based on a recurrent neural network (RNN)

Question 223

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A.Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
B.Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
C.Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
D.Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Question 224

A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

A.An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
B.Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C.AWS Glue with a custom ETL script to transform the data.
D.Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Question 225

A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.
Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

A.Amazon SageMaker seq2seq algorithm
B.Amazon SageMaker BlazingText algorithm in Skip-gram mode
C.Amazon SageMaker Object2Vec algorithm
D.Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
E.Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)