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最新的 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 免費考試真題 (Q22-Q27):

問題 #22
A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?

• A. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.
• B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.
• C. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
• D. Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

答案：B

 

問題 #23
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent Why could this be an issue for the linear least squares regression model?

• A. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
• B. It could create a singular matrix during optimization which fails to define a unique solution
• C. It could modify the loss function during optimization causing it to fail during training
• D. It could cause the backpropagation algorithm to fail during training

答案：B

解題說明：
* Linear least squares regression is a method of fitting a linear model to a set of data by minimizing the sum of squared errors between the observed and predicted values. The solution of the linear least squares problem can be obtained by solving the normal equations, which are given by ATAx=ATb, where A is the matrix of explanatory variables, b is the vector of response variables, and x is the vector of unknown coefficients.
* However, if the matrix A has two features that are perfectly linearly dependent, then the matrix ATA will be singular, meaning that it does not have a unique inverse. This implies that the normal equations do not have a unique solution, and the linear least squares problem is ill-posed. In other words, there are infinitely many values of x that can satisfy the normal equations, and the linear model is not identifiable.
* This can be an issue for the linear least squares regression model, as it can lead to instability, inconsistency, and poor generalization of the model. It can also cause numerical difficulties when trying to solve the normal equations using computational methods, such as matrix inversion or decomposition.
Therefore, it is advisable to avoid or remove the linearly dependent features from the matrix A before applying the linear least squares regression model.
Linear least squares (mathematics)
Linear Regression in Matrix Form
Singular Matrix Problem

 

問題 #24
A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).
The company developed a similar model previously but trained the model to classify a different set of objects.
The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.
Which combination of steps will accomplish this goal with the LEAST amount of effort? (Select TWO.)

• A. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
• B. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
• C. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
• D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
• E. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.

答案：B,D

解題說明：
To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.
In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):
CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks.
Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.
RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.
This approach minimizes training time and computational resources while effectively adapting the model to new tasks.

 

問題 #25
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?

• A. A longer training time
• B. Using a different optimizer
• C. Making the network larger
• D. Using some form of regularization

答案：D

解題說明：
Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:
Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.
Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.
Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.
The other options are not likely to fix the issue of overfitting, and may even worsen it:
A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.
Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.
Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.
References:
Regularization (machine learning)
Image Classification: Regularization
How to Reduce Overfitting With Dropout Regularization in Keras

 

問題 #26
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?

• A. Any of the task nodes
• B. Both core and task nodes
• C. Any of the core nodes
• D. Master node

答案：A

解題說明：
Explanation
The best option for using Spot Instances in a long-running Amazon EMR cluster is to use them for the task nodes. Task nodes are optional nodes that are used to increase the processing power of the cluster. They do not store any data and can be added or removed without affecting the cluster's operation. Therefore, they are more resilient to interruptions caused by Spot Instance termination. Using Spot Instances for the master node or the core nodes is not recommended, as they store important data and metadata for the cluster. If they are terminated, the cluster may fail or lose data. References:
Amazon EMR on EC2 Spot Instances
Instance purchasing options - Amazon EMR

 

問題 #27
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