Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2026-04-30.q178 Actual Practice Test Engine for Free (Page 27)

Question 126

A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

A.Customize the built-in image classification algorithm to use Inception and use this for model training.
B.Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
C.Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
D.Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.
E.Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

Correct Answer: C,D

Explanation
The best options to use an Inception neural network architecture instead of a ResNet architecture for image classification in Amazon SageMaker are:
Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training. This option allows users to customize the training environment and use any TensorFlow model they want. Users can create a Docker image that contains the TensorFlow Estimator API and the Inception model from the TensorFlow Hub, and push it to Amazon ECR. Then, users can use the SageMaker Estimator class to train the model using the custom Docker image and the training data from Amazon S3.
Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training. This option allows users to use the built-in TensorFlow container provided by SageMaker and write custom code to load and train the Inception model. Users can use the TensorFlow Estimator class to specify the custom code and the training data from Amazon S3. The custom code can use the TensorFlow Hub module to load the Inception model and fine-tune it on the training data.
The other options are not feasible for this scenario because:
Customize the built-in image classification algorithm to use Inception and use this for model training.
This option is not possible because the built-in image classification algorithm in SageMaker does not support customizing the neural network architecture. The built-in algorithm only supports ResNet models with different depths and widths.
Create a support case with the SageMaker team to change the default image classification algorithm to Inception. This option is not realistic because the SageMaker team does not provide such a service.
Users cannot request the SageMaker team to change the default algorithm or add new algorithms to the built-in ones.
Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker. This option is not advisable because it does not leverage the benefits of SageMaker, such as managed training and deployment, distributed training, and automatic model tuning. Users would have to manually install and configure the Inception network code and the TensorFlow framework on the EC2 instance, and run the training and inference code on the same instance, which may not be optimal for performance and scalability.
References:
Use Your Own Algorithms or Models with Amazon SageMaker
Use the SageMaker TensorFlow Serving Container
TensorFlow Hub

Question 127

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

* 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.
Image Augmentation - Amazon SageMaker
Amazon Rekognition Custom Labels Features
[Handling Imbalanced Datasets in Machine Learning]
[Semantic Segmentation - Amazon SageMaker]
[DetectLabels - Amazon Rekognition]
[Image Classification - MXNet - Amazon SageMaker]
[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

Question 128

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.
The solution needs to do the following:
* Calculate an anomaly score for each web traffic entry.
* Adapt unusual event identification to changing web patterns over time.
Which approach should the data scientist implement to meet these requirements?

A.Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.
B.Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.
C.Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
D.Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

Correct Answer: D

Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data in real time using SQL queries. Amazon Random Cut Forest (RCF) is a SQL extension that enables anomaly detection on streaming data. RCF is an unsupervised machine learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. A sliding window is a type of window that moves along with the data stream, so that the anomaly detection model can adapt to changing patterns over time. A tumbling window is a type of window that has a fixed size and does not overlap with other windows, so that the anomaly detection model is based on a fixed period of time. Therefore, option D is the best approach to meet the requirements of the question, as it uses RCF to calculate anomaly scores for each web traffic entry and uses a sliding window to adapt to changing web patterns over time.
Option A is incorrect because Amazon SageMaker Random Cut Forest (RCF) is a built-in model that can be used to train and deploy anomaly detection models on batch or streaming data, but it requires more steps and resources than using the RCF SQL extension in Amazon Kinesis Data Analytics. Option B is incorrect because Amazon SageMaker XGBoost is a built-in model that can be used for supervised learning tasks such as classification and regression, but not for unsupervised learning tasks such as anomaly detection. Option C is incorrect because k-Nearest Neighbors (kNN) is a SQL extension that can be used for classification and regression tasks on streaming data, but not for anomaly detection. Moreover, using a tumbling window would not allow the anomaly detection model to adapt to changing web patterns over time.
Using CloudWatch anomaly detection
Anomaly Detection With CloudWatch
Performing Real-time Anomaly Detection using AWS
What Is AWS Anomaly Detection? (And Is There A Better Option?)

Question 129

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.Poisson distribution ,
B.Uniform distribution
C.Normal distribution
D.Binomial distribution

Question 130

A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

A.Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
B.Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
C.Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
D.Customize the built-in image classification algorithm to use Inception and use this for model training.
E.Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

Question 126

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Question 128

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