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Updated and Error-free Itcertkey Amazon AWS-Certified-Machine-Learning-Specialty Exam Questions

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The AWS Certified Machine Learning - Specialty certification exam consists of 65 multiple-choice and multiple-answer questions that must be completed within 180 minutes. AWS-Certified-Machine-Learning-Specialty Exam is available in English, Japanese, Korean, and Simplified Chinese. The cost of the exam is $300.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q203-Q208):

NEW QUESTION # 203
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A. indicate a copy of the samples in the test database in the training dataset
B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
C. Change the cost function so that false negatives have a higher impact on the cost value than false positives
D. Add more deep trees to the random forest to enable the model to learn more features.
E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: B,C

NEW QUESTION # 204
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

A. Store datasets as tables in a multi-node Amazon Redshift cluster.
B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
C. Store datasets as global tables in Amazon DynamoDB.
D. Store datasets as files in Amazon S3.

Answer: A

NEW QUESTION # 205
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

A. A stratified k-fold cross-validation strategy with k=5
B. An 80/20 stratified split between training and validation
C. A k-fold cross-validation strategy with k=5 and 3 repeats
D. A k-fold cross-validation strategy with k=5

Answer: A

Explanation:
A stratified k-fold cross-validation strategy is a technique that preserves the class distribution in each fold. This is important for imbalanced datasets, such as the one in the question, where the disease is seen in only 3% of the population. If a random k-fold cross-validation strategy is used, some folds may have no positive cases or very few, which would lead to poor estimates of the model performance. A stratified k-fold cross-validation strategy ensures that each fold has the same proportion of positive and negative cases as the whole dataset, which makes the evaluation more reliable and robust. A k-fold cross-validation strategy with k=5 and 3 repeats is also a possible option, but it is more computationally expensive and may not be necessary if the stratification is done properly. An 80/20 stratified split between training and validation is another option, but it uses less data for training and validation than k-fold cross-validation, which may result in higher variance and lower accuracy of the estimates. References:
AWS Machine Learning Specialty Certification Exam Guide
AWS Machine Learning Training: Model Evaluation
How to Fix k-Fold Cross-Validation for Imbalanced Classification

NEW QUESTION # 206
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. Random Cut Forest (RCF)
B. Seq2seq
C. K-means
D. XGBoost

Answer: D

Explanation:
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.
References:
* XGBoost Algorithm
* Use XGBoost for Binary Classification with Amazon SageMaker
* Seq2seq Algorithm
* K-means Algorithm
* [Random Cut Forest Algorithm]

NEW QUESTION # 207
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
B. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
C. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
D. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Answer: B

NEW QUESTION # 208
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