MLA-C01 practice questions 171–180

1. Question 171 · 226 in bank

   A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance. Which solution will meet these requirements?

   • ASchedule a monitoring job in Amazon SageMaker Model Monitor. Generate the monitoring results for the model and data.
   • BCreate an Amazon CloudWatch dashboard that includes the metrics for processing steps in the nightly batch inference job. Compare the baseline resource metrics. Share the dashboard link.
   • CUse AWS Glue DataBrew to create a custom recipe job that uses the Numerical Statistics data quality check for the model file. Generate the results.
   • DCreate a SageMaker AI pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data.

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   Answer: A

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2. Question 172 · 226 in bank

   A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always be up to date with the latest changes in the source systems. Which solution will meet these requirements?

   • AUse direct connections to import data from the data sources into Data Wrangler.
   • BUse cataloged connections to import data from the data sources into Data Wrangler.
   • CUse AWS Glue to extract data from the data sources. Use AWS Glue also to import the data directly into Data Wrangler.
   • DUse AWS Lambda to extract data from the data sources. Use Lambda also to import the data directly into Data Wrangler.

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   Answer: B

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3. Question 173 · 226 in bank

   An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data. Which metric should the ML engineer use to evaluate the model’s performance?

   • AAccuracy
   • BInferenceLatency
   • CArea Under the ROC Curve (AUC)
   • DRoot mean square error (RMSE)

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   Answer: D

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4. Question 174 · 226 in bank

   A company has built, trained, and tuned two new ML models: • Model A detects if a transaction is fraudulent based on the IP address, location, and user credentials. This model will be accessed every time a transaction occurs. • Model B forecasts sales totals for the next month based on historical sales data. This model will be accessed one time each month. The company must deploy both models to production by using Amazon SageMaker AI. Which hosting solution for the models should the company use to meet these requirements?

   • AHost both models in one container behind one real-time endpoint.
   • BHost Model A with an asynchronous endpoint. Host Model B with a real-time endpoint.
   • CHost Model A with a real-time endpoint. Use batch transform for Model B.
   • DUse batch transform for Model A. Host Model B with an asynchronous endpoint.

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   Answer: C

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5. Question 175 · 226 in bank

   A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly supports. The model must be explainable to the bank’s regulators. Which modeling approach will meet these requirements?

   • ATrain the model by using the Object2Vec algorithm.
   • BTrain the model by using the linear learner algorithm.
   • CTrain a neural network.
   • DTrain the model by using the k-means algorithm.

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   Answer: B

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6. Question 176 · 226 in bank

   A company is preparing data to train a new ML model on Amazon SageMaker AI. The data has not been used before for ML training. The data includes duplicates and is missing some values. The company needs to increase the data quality and detect any statistical bias in the data. Which solution will meet these requirements?

   • AUse SageMaker Clarify to create data quality rules. Use SageMaker Model Monitor to detect bias.
   • BUse SageMaker Data Wrangler to create data quality rules. Use SageMaker Clarify to detect bias.
   • CUse SageMaker Debugger to create data quality rules. Use SageMaker Model Monitor to detect bias.
   • DUse SageMaker Model Monitor to create data quality rules. Use SageMaker Clarify to detect bias.

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   Answer: B

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7. Question 177 · 226 in bank

   An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment. The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment. Which solution will meet these requirements?

   • ARemove the outliers and perform a log transformation on the Square Meters variable.
   • BKeep the outliers and perform normalization on the Square Meters variable.
   • CRemove the outliers and perform one-hot encoding on the Square Meters variable.
   • DKeep the outliers and perform one-hot encoding on the Square Meters variable.

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   Answer: A

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8. Question 178 · 226 in bank

   A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and must display the results on a dashboard. An ML engineer creates a bias monitoring job. How should the ML engineer capture bias metrics to display on the dashboard?

   • ACapture AWS CloudTrail metrics from SageMaker Clarify.
   • BCapture Amazon CloudWatch metrics from SageMaker Clarify.
   • CCapture SageMaker Model Monitor metrics from Amazon EventBridge.
   • DCapture SageMaker Model Monitor metrics from Amazon Simple Notification Service (Amazon SNS).

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   Answer: B

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9. Question 179 · 226 in bank

   A company is using an Amazon SageMaker AI ML model to predict traffic accidents that potholes cause. An ML engineer has configured SageMaker Model Monitor to run as part of a SageMaker AI pipeline. In the MonitoringExecution output, the ML engineer observes several baseline_drift_check violations that are failing the pipeline. What should the ML engineer do to resolve this issue?

   • ARetrain the model by using a new SageMaker AI training job. Check for errors by using SageMaker Debugger.
   • BRetrain the model with new training data. Reuse the original baseline in Model Monitor.
   • CRetrain the model with new training data. Use the new baseline in Model Monitor.
   • DRerun the SageMaker AI pipeline after enabling the emit_metrics option in the baseline constraints file.

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   Answer: C

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10. Question 180 · 226 in bank

    A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?

    • AF1 score
    • BArea Under the ROC Curve (AUC)
    • CPrecision
    • DRecall

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    Answer: D

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MLA-C01 practice, question 171. 226 questions available.