Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2026-04-30.q178 Actual Practice Test Engine for Free (Page 36)

Question 171

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

A.The model needs to be completely re-engineered because it is unable to handle product inventory changes
B.The model's hyperparameters should be periodically updated to prevent drift
C.The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
D.The model should be periodically retrained using the original training data plus new data as product inventory changes

Question 172

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A.Create a NAT gateway within the corporate VPC.
B.Route Amazon SageMaker traffic through an on-premises network.
C.Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
D.Create VPC peering with Amazon VPC hosting Amazon SageMaker.

Question 173

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

Correct Answer: C,E,F

Explanation
Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.
Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.
Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model's ability to learn from the data.
Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.
Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.
Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model's convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.
References:
1: Image Augmentation - Amazon SageMaker
2: Amazon Rekognition Custom Labels Features
3: [Handling Imbalanced Datasets in Machine Learning]
4: [Semantic Segmentation - Amazon SageMaker]
5: [DetectLabels - Amazon Rekognition]
6: [Image Classification - MXNet - Amazon SageMaker]
7: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
8: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
9: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
10: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
11: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
12: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
13: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
14: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
15: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
16: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
17: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
18: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

Question 174

A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 TB in size.
The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.
Which solution meets these requirements?

A.Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
B.Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
C.Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
D.Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

Question 175

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

A.Perform data augmentation on the training data
B.Add vanishing gradient to the model
C.Use gradient checking in the model
D.Add L2 regularization to the model
E.Make the neural network architecture complex.

Question 171

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