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NEW QUESTION 23
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.)

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

Answer: A,B,E

Explanation:
Reference:
https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21
https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html

 

NEW QUESTION 24
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Binomial distributionB. Normal distributionC. Uniform distributionD. Poisson distribution ,

Answer: D

 

NEW QUESTION 25
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

A. Use CMD configin the Dockerfile to add the training program as a CMD of the imageB. Modify the bash_profile file in the container and add a bashcommand to start the training programC. Configure the training program as an ENTRYPOINTnamed trainD. Copy the training program to directory /opt/ml/train

Answer: A

 

NEW QUESTION 26
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendationsB. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendationsC. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendationsD. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: C

 

NEW QUESTION 27
For the given confusion matrix, what is the recall and precision of the model?

A. Recall = 0.92 Precision = 0.84B. Recall = 0.8 Precision = 0.92C. Recall = 0.84 Precision = 0.8D. Recall = 0.92 Precision = 0.8

Answer: D

 

NEW QUESTION 28
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