MLS-C01 Question Bank · Latest 50 Questions Free Preview (Page 5/5)
Questions 41–50 of the latest 50 MLS-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 380 questions online.
Question 41 · No. 371 · Single choice
A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables. The data scientist wants to understand the variance in the data along various directions in the feature space. Which solution will meet these requirements?
- A. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.
- B. Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.
- C. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.
- D. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.
Answer: C
Question 42 · No. 372 · Multiple choice
A company has 2,000 retail stores. The company needs to develop a new model to predict demand based on holidays and weather conditions. The model must predict demand in each geographic area where the retail stores are located. Before deploying the newly developed model, the company wants to test the model for 2 to 3 days. The model needs to be robust enough to adapt to supply chain and retail store requirements. Which combination of steps should the company take to meet these requirements with the LEAST operational overhead? (Choose two.)
- A. Develop the model by using the Amazon Forecast Prophet model.
- B. Develop the model by using the Amazon Forecast holidays featurization and weather index.
- C. Deploy the model by using a canary strategy that uses Amazon SageMaker and AWS Step Functions.
- D. Deploy the model by using an A/B testing strategy that uses Amazon SageMaker Pipelines.
- E. Deploy the model by using an A/B testing strategy that uses Amazon SageMaker and AWS Step Functions.
Answer: B, C
Explanation: 选项B和C的组合能够满足公司需求并减少运营开销。使用Amazon Forecast的节假日特征化和天气指数（选项B）可以确保模型在预测时考虑了这些因素。使用金丝雀部署策略（选项C）通过Amazon SageMaker和AWS Step Functions可以帮助公司以最小的运营开销测试和逐步推广模型。
Question 43 · No. 373 · Single choice
A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data. The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientist also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data. Which solution will meet these requirements with the LEAST amount of compute resources?
- A. Import the data by using the None option.
- B. Import the data by using the Stratified option.
- C. Import the data by using the First K option. Infer the value of K from domain knowledge.
- D. Import the data by using the Randomized option. Infer the random size from domain knowledge.
Answer: C
Question 44 · No. 374 · Single choice
A finance company has collected stock return data for 5,000 publicly traded companies. A financial analyst has a dataset that contains 2,000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns. Which solution will meet these requirements with the LEAST operational overhead?
- A. Use the linear leaner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.
- B. Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.
- C. Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick mode's feature importance scores.
- D. Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.
Answer: D
Explanation: Option D uses Amazon SageMaker Autopilot, which automates the process of applying machine learning to get insight from data. It handles the end-to-end process of building a model including feature selection, model training and tuning. This minimizes operational overhead as it requires minimal manual intervention. Options A, B, and C involve more manual steps and decisions, thus they would require more operational effort.
Question 45 · No. 375 · Single choice
A data scientist needs to develop a model to detect fraud. The data scientist has less data for fraudulent transactions than for legitimate transactions. The data scientist needs to check for bias in the model before finalizing the model. The data scientist needs to develop the model quickly. Which solution will meet these requirements with the LEAST operational overhead?
- A. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Studio Classic to develop the model. Use Amazon Augmented Al (Amazon A2I) to check the model for bias before finalizing the model.
- B. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Clarify to develop the model. Use Amazon Augmented AI (Amazon A2I) to check the model for bias before finalizing the model.
- C. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon SageMaker Studio. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Clarify to check the model for bias before finalizing the model.
- D. Process and reduce bias by using an Amazon SageMaker Studio notebook. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Model Monitor to check the model for bias before finalizing the model.
Answer: C
Explanation: 选项C是最佳选择，因为它使用了Amazon SageMaker Studio来处理和减少偏见，并且利用Amazon SageMaker JumpStart快速开发模型。此外，它还使用Amazon SageMaker Clarify来检查模型的偏见，确保在最终确定模型之前进行了适当的验证。这个选项提供的解决方案最有可能在最少的操作开销下满足需求。
Question 46 · No. 376 · Single choice
A data scientist uses Amazon SageMaker to perform hyperparameter tuning for a prototype machine leaming (ML) model. The data scientist's domain knowledge suggests that the hyperparameter is highly sensitive to changes. The optimal value, x, is in the 0.5 < x < 1.0 range. The data scientist's domain knowledge suggests that the optimal value is close to 1.0. The data scientist needs to find the optimal hyperparameter value with a minimum number of runs and with a high degree of consistent tuning conditions. Which hyperparameter scaling type should the data scientist use to meet these requirements?
- A. Auto scaling
- B. Linear scaling
- C. Logarithmic scaling
- D. Reverse logarithmic scaling
Answer: D
Question 47 · No. 377 · Single choice
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution. After training, the model's inferences accuracy is lower than expected. Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?
- A. Normalize the problematic features.
- B. Bootstrap the problematic features.
- C. Remove the problematic features.
- D. Extrapolate synthetic features.
Answer: A
Explanation: Normalize the problematic features will result in the GREATEST increase of the model's inference accuracy because it adjusts the scale of the features to a standard range, which can help mitigate the impact of the significantly greater values and improve model performance.
Question 48 · No. 378 · Single choice
An ecommerce company has observed that customers who use the company's website rarely view items that the website recommends to customers. The company wants to recommend items to customers that customers are more likely to want to purchase. Which solution will meet this requirement in the SHORTEST amount of time?
- A. Host the company's website on Amazon EC2 Accelerated Computing instances to increase the website response speed.
- B. Host the company's website on Amazon EC2 GPU-based instances to increase the speed of the website's search tool.
- C. Integrate Amazon Personalize into the company's website to provide customers with personalized recommendations.
- D. Use Amazon SageMaker to train a Neural Collaborative Filtering (NCF) model to make product recommendations.
Answer: C
Explanation: 选项 C 是最符合要求的解决方案，因为它可以直接集成到公司的网站中，并提供个性化的推荐，从而满足公司快速提升推荐准确性的需求。选项 A 和 B 虽然可以提高网站的响应速度和搜索工具的速度，但它们并没有直接解决如何提供更符合用户需求的商品推荐问题。选项 D 虽然通过训练模型也可以改善推荐效果，但需要更多的时间来开发和部署。
Question 49 · No. 379 · Single choice
A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions. The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualization Which solution will meet these requirements?
- A. Use the Amazon SageMaker Data Wrangler bar chart feature. Use Group By to represent the third and fourth dimensions.
- B. Use the Amazon SageMaker Canvas box plot visualization Use color and fill pattern to represent the third and fourth dimensions
- C. Use the Amazon SageMaker Data Wrangler histogram feature Use color and fill pattern to represent the third and fourth dimensions
- D. Use the Amazon SageMaker Canvas scatter plot visualization Use scatter point size and color to represent the third and fourth dimensions
Answer: D
Explanation: The question specifies that the ML specialist wants to visualize four dimensions, with the first two as coordinates (x and y axes), the third as color, and the fourth as size. Among the options provided, only the scatter plot visualization in Amazon SageMaker Canvas allows for this type of representation. Scatter plots are inherently designed to show data points across two dimensions using coordinates, and they can incorporate additional dimensions via attributes like color and size. Therefore, option D is the correct solution.
Question 50 · No. 380 · Single choice
A clothing company is experimenting with different colors and materials for its products. The company stores the entire sales history of all its products in Amazon S3. The company is using custom-built exponential smoothing (ETS) models to forecast demand for its current products. The company needs to forecast the demand for a new product variation that the company will launch soon. Which solution will meet these requirements?
- A. Train a custom ETS model.
- B. Train an Amazon SageMaker DeepAR model.
- C. Train an Amazon SageMaker К-means clustering model.
- D. Train a custom XGBoost model.
Answer: B
Explanation: The company needs to forecast demand for a new product variation, and the DeepAR model in Amazon SageMaker is specifically designed for forecasting time-series data, especially when historical data of similar products is available. Unlike custom ETS models or other machine learning models like XGBoost, DeepAR can leverage the sales history of existing products in Amazon S3 to generate accurate forecasts for the new product variation. K-means clustering is not suitable here because it is used for grouping data rather than forecasting.