ExamTopics Logo
Mail Us[email protected]
Menu
Amazon Discussions
exam questions

Exam AWS Certified Machine Learning - Specialty All Questions

View all questions & answers for the AWS Certified Machine Learning - Specialty exam
Go to Exam

Exam AWS Certified Machine Learning - Specialty topic 1 question 111 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 111
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 ֳ— 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

  • A. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object- detection single-shot multibox detector (SSD) algorithm.
  • C. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object- detection single-shot multibox detector (SSD) algorithm.
  • D. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by [deleted] at Feb. 5, 2021, 5:56 p.m.
Disclaimers:
  • - ExamTopics website is not related to, affiliated with, endorsed or authorized by Amazon.
  • - Trademarks, certification & product names are used for reference only and belong to Amazon.

Comments

Chosen Answer:
This is a voting comment (?). It is better to Upvote an existing comment if you don't have anything to add.
Switch to a voting comment New
Submit Cancel
SophieSu
Highly Voted 3 years, 6 months ago
C is my answer. Pay attention that the question is asking for 2 things: 1. detect specific types of weeds 2. detect the location of each type within the field. Image Classification can only classify images. Object detection algorithm: 1.identifies all instances of objects within the image scene. 2.its location and scale in the image are indicated by a rectangular bounding box. Data format for Computer Vision algorithms in SageMaker: Recommend to use RecordIO.
upvoted 33 times
...
MultiCloudIronMan
Most Recent 7 months, 1 week ago
Selected Answer: C
RecordIO Format: This format is efficient for storing and processing large datasets, which is beneficial for training deep learning models. Object Detection SSD Algorithm: This algorithm is designed to detect and locate multiple objects within an image, making it ideal for identifying and pinpointing various types of weeds in the field
upvoted 1 times
...
Mickey321
1 year, 8 months ago
Selected Answer: C
Record IO preffered and also object detection due to several types of weeds
upvoted 1 times
...
AjoseO
2 years, 2 months ago
Selected Answer: C
The goal is to detect specific types of weeds and their locations within a field, which is a task that requires object detection, rather than image classification. Object detection algorithms are designed to identify objects and their locations within an image, whereas image classification algorithms only categorize an entire image into various classes. Single-shot multibox detectors (SSD) are a type of object detection algorithm that are well-suited for real-time inferencing and have been shown to be effective for a variety of object detection tasks. By preparing the images in RecordIO format and using Amazon SageMaker, the company can easily train, test, and validate the model, making it easier to deploy the model in a scalable and secure environment.
upvoted 3 times
...
apprehensive_scar
3 years, 2 months ago
C is the right answer. you need to detect location
upvoted 3 times
...
AShahine21
3 years, 6 months ago
C You can detect the type of weeds and the location within the field.
upvoted 1 times
...
cnethers
3 years, 6 months ago
If they had an answer with "Faster R-CNN" then it would be different. This is a good article talking about SSD, Faster R-CNN, R-FCN and others which is a good read. https://jonathan-hui.medium.com/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359
upvoted 2 times
...
cnethers
3 years, 7 months ago
I would go with answer C .. SSD are new architectures faster than the old CNN https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab
upvoted 2 times
...
[Removed]
3 years, 7 months ago
I would select answer A, situation is very similar to this one: https://aws.amazon.com/blogs/machine-learning/building-a-lawn-monitor-and-weed-detection-solution-with-aws-machine-learning-and-iot-services/
upvoted 3 times
crispogioele
3 years, 6 months ago
I think it's better go with C, since the question also ask for the location of the weed on the field, while the example you posted is just a classifier.
upvoted 1 times
attaraya
3 years, 5 months ago
since field is divided in to 10x10 grid I felt A is more suitable.
upvoted 1 times
cpal012
2 years ago
So you expect precisely one weed per grid (total 100) of an entire field? If a field is a hectare, then each grid would be 100m2
upvoted 1 times
...
...
...
Bala1212081
3 years, 6 months ago
The llink says clearly only classification and not talking about object detection. Answer should be C
upvoted 1 times
...
...
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.

Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.

SaveCancel
exam
Someone Bought Contributor Access for:
SY0-701
London, 1 minute ago