MLS-C01 practice questions 291–300

1. Question 291 · 368 in bank

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   A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions. After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model. Which solutions will meet this requirement? (Choose two.)

   • AIncrease the number of passes on the existing training data. Perform more hyperparameter tuning.
   • BIncrease the amount of regularization. Use fewer feature combinations.
   • CAdd new domain-specific features. Use more complex models.
   • DUse fewer feature combinations. Decrease the number of numeric attribute bins.
   • EDecrease the amount of training data examples. Reduce the number of passes on the existing training data.

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   Answer: AC

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2. Question 292 · 368 in bank

   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?

   • AAuto scaling
   • BLinear scaling
   • CLogarithmic scaling
   • DReverse logarithmic scaling

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   Answer: D

   Explanation

   This approach allocates more search effort near the higher end of the range, ensuring that values closer to 1.0 are explored more thoroughly, thus meeting the need for a minimum number of runs while maintaining consistent tuning conditions

   Source: Adapted from community discussion — MultiCloudIronMan

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3. Question 293 · 368 in bank

   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?

   • AUse 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.
   • BUse 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.
   • CUse 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.
   • DUse the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

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   Answer: C

   References

   • AWS documentation

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4. Question 294 · 368 in bank

   A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers. Which solution will meet these requirements with the LEAST operational effort?

   • AUse Amazon SageMaker to approve transactions only for products the company has sold in the past.
   • BUse Amazon SageMaker to train a custom fraud detection model based on customer data.
   • CUse the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent.
   • DUse the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

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   Answer: C

   Explanation

   This approach ensures that fraudulent transactions are effectively rejected, even if it means losing some profitable transactions, aligning with the company's risk mitigation strategy.

   Source: Adapted from community discussion — MultiCloudIronMan

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5. Question 295 · 368 in bank

   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?

   • AProcess 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.
   • BProcess 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.
   • CProcess 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.
   • DProcess 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.

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   Answer: C

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6. Question 296 · 368 in bank

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   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.)

   • ADevelop the model by using the Amazon Forecast Prophet model.
   • BDevelop the model by using the Amazon Forecast holidays featurization and weather index.
   • CDeploy the model by using a canary strategy that uses Amazon SageMaker and AWS Step Functions.
   • DDeploy the model by using an A/B testing strategy that uses Amazon SageMaker Pipelines.
   • EDeploy the model by using an A/B testing strategy that uses Amazon SageMaker and AWS Step Functions.

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   Answer: BC

   Explanation

   These steps ensure that the model is developed with relevant features and deployed in a way that minimizes operational risks and overhead.

   Source: Adapted from community discussion — MultiCloudIronMan

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7. Question 297 · 368 in bank

   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?

   • AUse 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.
   • BUse random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.
   • CUse 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.
   • DUse 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.

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   Answer: D

   Explanation

   This approach leverages the automation capabilities of SageMaker Autopilot and the detailed analysis provided by SageMaker Clarify, ensuring an efficient and effective solution for identifying the most valuable attributes for predicting stock returns.

   Source: Adapted from community discussion — MultiCloudIronMan

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8. Question 298 · 368 in bank

   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?

   • AUse the Amazon SageMaker Data Wrangler bar chart feature. Use Group By to represent the third and fourth dimensions.
   • BUse the Amazon SageMaker Canvas box plot visualization Use color and fill pattern to represent the third and fourth dimensions
   • CUse the Amazon SageMaker Data Wrangler histogram feature Use color and fill pattern to represent the third and fourth dimensions
   • DUse the Amazon SageMaker Canvas scatter plot visualization Use scatter point size and color to represent the third and fourth dimensions

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   Answer: D

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9. Question 299 · 368 in bank

   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?

   • ATrain a custom ETS model.
   • BTrain an Amazon SageMaker DeepAR model.
   • CTrain an Amazon SageMaker К-means clustering model.
   • DTrain a custom XGBoost model.

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   Answer: B

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10. Question 300 · 368 in bank

    A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake. The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of: ✑ Real-time analytics ✑ Interactive analytics of historical data ✑ Clickstream analytics ✑ Product recommendations Which services should the Specialist use?

    • AAWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
    • BAmazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
    • CAWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
    • DAmazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

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    Answer: A

    Explanation

    Athena can do Interactive analytics on Historical data, but here its only use is "Athena as the data catalog" and this is the work of Glue data catalog using its crawlers, so it cannot be B or D. --So its either A or C -- Now Kinesis data Streams/Analytics is know for real time data analytics but if it is reading from data already stored in S3 using DMS then we can say it is getting historical data. -- Here I am not very clear if Kinesis part will happen on incoming data before S3 or After data persists to S3 and Kinesis reads it through S3-->DMS--Kinesis data stream -- Kinesis analytics-->Firehose. But still insights are always on real-time/current data based on historical data trends , so the statement in C "Analytics for historical data insights" is in-correct in general . Hence ANSWER is :A

    Source: Adapted from community discussion — harmanbirstudy

    References

    • AWS documentation

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MLS-C01 practice, question 291. 368 questions available.