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AWS-Certified-Machine-Learning-Specialty考試 - AWS-Certified-Machine-Learning-Specialty考試大綱

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Amazon AWS-Certified-Machine-Learning-Specialty 考試大綱：

主題	簡介
主題 1	Select The Appropriate Model(S) For A Given Machine Learning Problem
主題 2	Evaluate Machine Learning Models Perform Hyperparameter Optimization
主題 3	Identify And Implement A Data-Transformation Solution Perform Feature Engineering
主題 4	Apply Basic AWS Security Practices To Machine Learning Solutions
主題 5	Build Machine Learning Solutions For Performance, Availability, Scalability, Resiliency, And Fault Tolerance
主題 6	Exploratory Data Analysis2.1Sanitize And Prepare Data For Modeling

最新的 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 免費考試真題 (Q81-Q86):

問題 #81
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

• A. Create a new feature space using principal component analysis (PCA)
• B. Apply the Pearson correlation coefficient
• C. Use matrix multiplication on highly correlated features.
• D. Perform one-hot encoding on highly correlated features

答案：D
問題 #82
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

• A. Single Perceptron with sigmoidal activation function
• B. Decision tree
• C. Naive Bayesian classifier
• D. Linear support vector machine (SVM)

答案：B 解題說明：
To identity fraudulent or not, you need a model with high accuracy and high recall (low false negative). The data in the graph is non-linear. Generally Decision tree gives better result for non- linear data than Naive Bayes classifier.
https://datascience.stackexchange.com/questions/6787/are-decision-tree-algorithms-linear-or- nonlinear
https://sebastianraschka.com/Articles/2014naivebayes_1.html
問題 #83
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. K-means
• C. XGBoost
• D. Seq2seq

答案：B
問題 #84
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.

Which model would have the HIGHEST accuracy?

• A. Decision tree
• B. Single perceptron with a Tanh activation function
• C. Support vector machine (SVM) with a radial basis function kernel
• D. Linear support vector machine (SVM)

答案：C
問題 #85
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing The Data Scientist has been given the following requirements for the cloud solution
* Combine multiple data sources
* Reuse existing PySpark logic
* Run the solution on the existing schedule
* Minimize the number of servers that will need to be managed
Which architecture should the Data Scientist use to build this solution?

• A. Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
• B. Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a "processed" location m Amazon S3 that is accessible tor downstream use
• C. Use Amazon Kinesis Data Analytics to stream the input data and perform realtime SQL queries against the stream to carry out the required transformations within the stream Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use
• D. Write the raw data to Amazon S3 Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3 Write the Lambda logic in Python and implement the existing PySpartc logic to perform the ETL process Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use

答案：B
問題 #86
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