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Die AWS -zertifizierte maschinelle Lernen - Spezialprüfung ist für Personen gedacht, die ein starkes Verständnis für maschinelles Lernen haben, einschließlich Deep -Lernen und neuronalen Netzwerken, und die Erfahrung mit Erfahrung in der Gestaltung, Implementierung und Bereitstellung von Lösungen für maschinelles Lernen auf der AWS -Plattform haben. Diese Zertifizierung ist besonders wertvoll für Datenwissenschaftler, Softwareentwickler und andere IT -Fachkräfte, die ihr Fachwissen im maschinellen Lernen demonstrieren und sich in einem wettbewerbsfähigen Arbeitsmarkt unterscheiden. Mit dieser Zertifizierung können Kandidaten potenziellen Arbeitgebern und Kunden ihre Fähigkeiten vorstellen und Zugang zu exklusiven AWS -Ressourcen und Networking -Möglichkeiten erhalten.

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Die Amazon AWS-zertifizierte Machine-Learning-Spezifikation (AWS Certified Machine Learnt-Specialty) wurde entwickelt, um die Fähigkeiten und das Wissen des Kandidaten über das Erstellen, Entwerfen, Bereitstellen und Wartung von Lösungen für maschinelles Lernen (ML) mithilfe von Amazon-Webdiensten (Amazon Web Services (maschinelles Lernen) zu validieren. AWS). Diese Zertifizierungsprüfung ist ideal für Fachkräfte, die an einer Karriere im Bereich der KI und ML interessiert sind, oder für diejenigen, die ihre bestehenden Fähigkeiten auf diesem Gebiet verbessern möchten. Die AWS Certified Machine Learning - Specialty Certification wird weltweit anerkannt und ist ein Beweis für das Fachwissen des Kandidaten im Bereich ML.

Die Zertifizierungsprüfung deckt eine breite Palette von Themen ab, einschließlich Datenvorbereitung, Modelltraining, Modellbereitstellung und Algorithmen für maschinelles Lernen. Die Prüfung wurde entwickelt, um das Wissen des Kandidaten über AWS-Dienste und ihre Fähigkeit zu testen, maschinelle Lerntechniken auf reale Probleme anzuwenden. Die Prüfung besteht aus Multiple-Choice- und Multiple-Response-Fragen, und der Kandidat hat 170 Minuten Zeit, um sie zu vervollständigen.

Amazon AWS Certified Machine Learning - Specialty MLS-C01 Prüfungsfragen mit Lösungen (Q116-Q121):

116. Frage
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

• A. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
• B. The precision of the model is 86%, which is less than the accuracy of the model.
• C. The precision of the model is 86%, which is greater than the accuracy of the model.
• D. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

Antwort: D

 

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

Antwort: B,C

Begründung:
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.

 

118. Frage
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
• B. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
• C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
• D. Configure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.

Antwort: C

Begründung:
To read data from an Amazon S3 bucket that is protected with server-side encryption using AWS KMS, the Amazon SageMaker notebook instance needs to have an IAM role that has permission to access the S3 bucket and the KMS key. The IAM role is an identity that defines the permissions for the notebook instance to interact with other AWS services. The IAM role can be assigned to the notebook instance when it is created or updated later.
The KMS key policy is a document that specifies who can use and manage the KMS key. The KMS key policy can grant permission to the IAM role of the notebook instance to decrypt the data in the S3 bucket. The KMS key policy can also grant permission to other principals, such as AWS accounts, IAM users, or IAM roles, to use the KMS key for encryption and decryption operations.
Therefore, the Machine Learning Specialist should assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role. This way, the notebook instance can use the IAM role credentials to access the S3 bucket and the KMS key, and read the encrypted data from the S3 bucket.
References:
Create an IAM Role to Grant Permissions to Your Notebook Instance
Using Key Policies in AWS KMS

 

119. Frage
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. Uniform distribution
• B. Binomial distribution
• C. Normal distribution
• D. Poisson distribution

Antwort: D

Begründung:
The Poisson distribution is used to model the number of events occurring within a given time interval.

 

120. Frage
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

• A. K-means
• B. Seq2seq
• C. XGBoost
• D. Random Cut Forest (RCF)

Antwort: C

Begründung:
XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems.
Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.
XGBoost is suitable for the credit card fraud detection problem for the following reasons:
* The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
* The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
* The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.
The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:
* Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
* K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
* Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.
XGBoost Algorithm
Use XGBoost for Binary Classification with Amazon SageMaker
Seq2seq Algorithm
K-means Algorithm
[Random Cut Forest Algorithm]

 

121. Frage
......

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