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The AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics, including exploring data, building and training ML models, deploying models, and managing and optimizing ML solutions. AWS-Certified-Machine-Learning-Specialty Exam also tests the candidate's knowledge of machine learning algorithms, deep learning, neural networks, and other related technologies. Passing this certification exam requires a deep understanding of AWS ML services, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q260-Q265):

NEW QUESTION # 260
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

• A. Rectified linear units (ReLU)
• B. Dropout
• C. Softmax
• D. Smooth L1 loss

Answer: C

Explanation:
Explanation
The softmax function is a function that can transform a vector of arbitrary real values into a vector of real values in the range (0,1) that sum to 1. This means that the softmax function can produce a valid probability distribution over multiple classes. The softmax function is often used as the activation function of the output layer in a neural network, especially for multi-class classification problems. The softmax function can assign higher probabilities to the classes with higher scores, which allows the network to make predictions based on the most likely class. In this case, the Machine Learning Specialist wants to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes of animals. Therefore, the softmax function is the most suitable function to produce the desired output.
References:
Softmax Activation Function for Deep Learning: A Complete Guide
What is Softmax in Machine Learning? - reason.town
machine learning - Why is the softmax function often used as activation ...
Multi-Class Neural Networks: Softmax | Machine Learning | Google for ...

NEW QUESTION # 261
A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.
Which modeling approach will deliver the MOST accurate prediction of product quality?

• A. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
• B. Amazon SageMaker DeepAR forecasting algorithm
• C. A convolutional neural network (CNN) and ResNet
• D. Amazon SageMaker XGBoost algorithm

Answer: C

Explanation:
Explanation
A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.
References:
Convolutional Neural Networks (CNNs / ConvNets)
PyTorch ResNet: The Basics and a Quick Tutorial

NEW QUESTION # 262
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

• A. Organize the Docker container's file structure to execute on GPU instances.
• B. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
• C. Bundle the NVIDIA drivers with the Docker image
• D. Build the Docker container to be NVIDIA-Docker compatible

Answer: D

Explanation:
Explanation
To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container's file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances.
References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker

NEW QUESTION # 263
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

• A. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
• B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
• C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
• D. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
• E. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

Answer: C,E

NEW QUESTION # 264
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 0
• B. 1
• C. 2
• D. 3

Answer: A

Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
* Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
* K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

NEW QUESTION # 265
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