An ecommerce company discovers that the search tool for the company's website is not presenting the top search results to customers. The company needs to resolve the issue so the search tool will present results that customers are most likely to want to purchase.
Which solution will meet this requirement with the LEAST operational effort?
A. Use the Amazon SageMaker BlazingText algorithm to add context to search results through query expansion.
B. Use the Amazon SageMaker XGBoost algorithm to improve candidate ranking.
C. Use Amazon CloudSearch and sort results by the search relevance score.
D. Use Amazon CloudSearch and sort results by the geographic location.
A machine learning (ML) specialist collected daily product usage data for a group of customers. The ML specialist appended customer metadata such as age and gender from an external data source.
The ML specialist wants to understand product usage patterns for each day of the week for customers in specific age groups. The ML specialist creates two categorical features named dayofweek and binned_age, respectively.
Which approach should the ML specialist use discover the relationship between the two new categorical features?
A. Create a scatterplot for day_of_week and binned_age.
B. Create crosstabs for day_of_week and binned_age.
C. Create word clouds for day_of_week and binned_age.
D. Create a boxplot for day_of_week and binned_age.
A telecommunications company has deployed a machine learning model using Amazon SageMaker. The model identifies customers who are likely to cancel their contract when calling customer service. These customers are then directed to a specialist service team. The model has been trained on historical data from multiple years relating to customer contracts and customer service interactions in a single geographic region.
The company is planning to launch a new global product that will use this model. Management is concerned that the model might incorrectly direct a large number of calls from customers in regions without historical data to the specialist service team.
Which approach would MOST effectively address this issue?
A. Enable Amazon SageMaker Model Monitor data capture on the model endpoint. Create a monitoring baseline on the training dataset. Schedule monitoring jobs. Use Amazon CloudWatch to alert the data scientists when the numerical distance of regional customer data fails the baseline drift check. Reevaluate the training set with the larger data source and retrain the model.
B. Enable Amazon SageMaker Debugger on the model endpoint. Create a custom rule to measure the variance from the baseline training dataset. Use Amazon CloudWatch to alert the data scientists when the rule is invoked. Reevaluate the training set with the larger data source and retrain the model.
C. Capture all customer calls routed to the specialist service team in Amazon S3. Schedule a monitoring job to capture all the true positives and true negatives, correlate them to the training dataset, and calculate the accuracy. Use Amazon CloudWatch to alert the data scientists when the accuracy decreases. Reevaluate the training set with the additional data from the specialist service team and retrain the model.
D. Enable Amazon CloudWatch on the model endpoint. Capture metrics using Amazon CloudWatch Logs and send them to Amazon S3. Analyze the monitored results against the training data baseline. When the variance from the baseline exceeds the regional customer variance, reevaluate the training set and retrain the model.
A car company has dealership locations in multiple cities. The company uses a machine learning (ML) recommendation system to market cars to its customers.
An ML engineer trained the ML recommendation model on a dataset that includes multiple attributes about each car. The dataset includes attributes such as car brand, car type, fuel efficiency, and price.
The ML engineer uses Amazon SageMaker Data Wrangler to analyze and visualize data. The ML engineer needs to identify the distribution of car prices for a specific type of car.
Which type of visualization should the ML engineer use to meet these requirements?
A. Use the SageMaker Data Wrangler scatter plot visualization to inspect the relationship between the car price and type of car.
B. Use the SageMaker Data Wrangler quick model visualization to quickly evaluate the data and produce importance scores for the car price and type of car.
C. Use the SageMaker Data Wrangler anomaly detection visualization to Identify outliers for the specific features.
D. Use the SageMaker Data Wrangler histogram visualization to inspect the range of values for the specific feature.
A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.
Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.
Which solution will meet these requirements?
A. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.
B. Instead of File mode, configure the SageMaker training job to use FastFile mode with no other changes.
C. Instead of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no other changes,
D. Instead of On-Demand Instances, configure the SageMaker training job to use Spot Instances, implement model checkpoints.
A banking company provides financial products to customers around the world. A machine learning (ML) specialist collected transaction data from internal customers. The ML specialist split the dataset into training, testing, and validation datasets. The ML specialist analyzed the training dataset by using Amazon SageMaker Clarify. The analysis found that the training dataset contained fewer examples of customers in the 40 to 55 year-old age group compared to the other age groups.
Which type of pretraining bias did the ML specialist observe in the training dataset?
A. Difference in proportions of labels (DPL)
B. Class imbalance (CI)
C. Conditional demographic disparity (CDD)
D. Kolmogorov-Smirnov (KS)
A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.
An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective
metric from the best training job.
Which completion criteria will meet this requirement?
A. MaxRuntimeInSeconds
B. TargetObjectiveMetricValue
C. CompleteOnConvergence
D. MaxNumberOfTrainingJobsNotImproving
A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company's readers. The company's readers are primarily located in a single city.
The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours. There is very little traffic at other times of day. The media company needs to minimize the time required to deliver recommendations to its readers. The expected amount of data that the API call will return for inference is less than 4 MB.
Which solution will meet these requirements in the MOST cost-effective way?
A. Real-time inference with auto scaling
B. Serverless inference with provisioned concurrency
C. Asynchronous inference
D. A batch transform task
A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run. The tuning jobs continue even when the jobs are not significantly improving against the objective metric.
The ML engineer needs the training jobs to optimize the hyperparameters more quickly.
How should the ML engineer configure the SageMaker AMT data types to meet these requirements?
A. Set Strategy to the Bayesian value.
B. Set RetryStrategy to a value of 1.
C. Set ParameterRanges to the narrow range Inferred from previous hyperparameter jobs.
D. Set TrainingJobEarlyStoppingType to the AUTO value.
A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.
A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.
Which solution will meet these requirements?
A. Apply anomaly detection to remove outliers from the training dataset before training.
B. Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.
C. Apply normalization to the features of the training dataset before training.
D. Apply undersampling to the training dataset before training.
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