Amazon AWS-Certified-Machine-Learning-Specialty Questions - Quick Tips To Pass [2025]

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q10-Q15):

NEW QUESTION # 10
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Enable early stopping
B. Choose a smaller learning rate
C. Enable dropout
D. Choose a higher number of layers
E. Choose a lower number of layers
F. Include all the images from the test set in the training set

Answer: A,C,E

Explanation:
The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:
* Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.
* Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.
* Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.
Deep Learning - Machine Learning Lens
How to Avoid Overfitting in Deep Learning Neural Networks
How to Identify Overfitting Machine Learning Models in Scikit-Learn

NEW QUESTION # 11
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
B. Enable network isolation for training jobs and models.
C. Use SCPs to restrict access to SageMaker.
D. Restrict notebook presigned URLs to specific IPs used by the company.
E. Disable root access on the SageMaker notebook instances.
F. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: A,B,F

Explanation:
To control data egress from SageMaker, the ML engineer can use the following mechanisms:
* Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1
* Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2
* Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker:
* Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4
* Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
* Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.
References:
* 1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
* 2: Network Isolation - Amazon SageMaker
* 3: Encrypt Data at Rest and in Transit - Amazon SageMaker
* 4: Using Service Control Policies - AWS Organizations
* : Disable Root Access - Amazon SageMaker
* : Create a Presigned Notebook Instance URL - Amazon SageMaker

NEW QUESTION # 12
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. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
B. 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.
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. Customize the built-in image classification algorithm to use Inception and use this for model training.

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

NEW QUESTION # 13
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's hyperparameters should be periodically updated to prevent drift
B. The model needs to be completely re-engineered because it is unable to handle product inventory changes
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

Answer: B

NEW QUESTION # 14
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?

A. Use the SageMaker batch transform feature to transform the training data into a DataFrame
B. Use AWS Glue to compress the data into the Apache Parquet format
C. Use the SageMaker hyperparameter optimization feature to automatically optimize the data
D. Transform the dataset into the Recordio protobuf format

Answer: D

Explanation:
The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:
Common Data Formats for Training
Using RecordIO Format
Content Types Supported by Built-in Algorithms

NEW QUESTION # 15
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AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY QUESTIONS - QUICK TIPS TO PASS [2025]

Amazon AWS-Certified-Machine-Learning-Specialty Questions - Quick Tips To Pass [2025]