MLS-C01考題寶典，MLS-C01真題材料你是其中之一嗎，你是否還在擔心和困惑的各種材料和花哨的培訓課程考試嗎？VCESoft是你正確的選擇，因為我們可以為你提供全面的考試資料，包括問題及答案，也是最精確的解釋，所有這些將幫助你掌握更好的知識，我們有信心你將通過VCESoft的Amazon的MLS-C01考試認證，這也是我們對所有客戶提供的保障。 最新的 AWS Certified Specialty MLS-C01 免費考試真題 (Q234-Q239):問題 #234
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?
A. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket
B. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
C. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances.
Store the resulting transcriptions in the transcription S3 bucket.
D. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
答案：B
問題 #235
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?
A. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
B. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
C. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
D. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.
答案：B
解題說明：
The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:
Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.
Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.
Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.
References:
1: AWS Machine Learning Specialty Exam Guide
2: AWS Machine Learning Specialty Course
3: AWS Machine Learning Blog
問題 #236
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 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 modify the loss function during optimization, causing it to fail during training.
B. It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model.
C. It could cause the backpropagation algorithm to fail during training.
D. It could create a singular matrix during optimization, which fails to define a unique solution.
答案：D
解題說明：
In linear least squares regression, the design matrix (often denoted as XXX) must have full rank to ensure a unique solution. When two or more features are perfectly linearly dependent, it leads to multicollinearity, causing the matrix XTXX
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