MLS-C01 Question Bank · Latest 50 Questions Free Preview (Page 2/5)

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain. The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain. The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department. How should the company meet these requirements?

• A. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.
• B. Use the SageMaker CreateHumanTaskUi API to generate a UI URL. Pass the URL to the proxy application.
• C. Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.
• D. Use the SageMaker CreatePresignedNotebooklnstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.

Answer: A

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset. Which solution will meet this requirement with the LEAST development effort?

• A. Use SageMaker Data Wrangler to perform a Gini importance score analysis.
• B. Use a SageMaker notebook instance to perform principal component analysis (PCA).
• C. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
• D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

Answer: A

A data scientist is trying to improve the accuracy of a neural network classification model. The data scientist wants to run a large hyperparameter tuning job in Amazon SageMaker. However, previous smaller tuning jobs on the same model often ran for several weeks. The ML specialist wants to reduce the computation time required to run the tuning job. Which actions will MOST reduce the computation time for the hyperparameter tuning job? (Choose two.)

• A. Use the Hyperband tuning strategy.
• B. Increase the number of hyperparameters.
• C. Set a lower value for the MaxNumberOfTrainingJobs parameter.
• D. Use the grid search tuning strategy.
• E. Set a lower value for the MaxParallelTrainingJobs parameter.

Answer: A, C

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced. What should the engineer do to improve the validation accuracy of the model?

• A. Perform stratified sampling on the original dataset.
• B. Acquire additional data about the majority classes in the original dataset.
• C. Use a smaller, randomly sampled version of the training dataset.
• D. Perform systematic sampling on the original dataset.

Answer: A

A data scientist is conducting exploratory data analysis (EDA) on a dataset that contains information about product suppliers. The dataset records the country where each product supplier is located as a two-letter text code. For example, the code for New Zealand is "NZ." The data scientist needs to transform the country codes for model training. The data scientist must choose the solution that will result in the smallest increase in dimensionality. The solution must not result in any information loss. Which solution will meet these requirements?

• A. Add a new column of data that includes the full country name.
• B. Encode the country codes into numeric variables by using similarity encoding.
• C. Map the country codes to continent names.
• D. Encode the country codes into numeric variables by using one-hot encoding.

Answer: B

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.

Answer: B

A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features. How should the ML engineer predict the contribution of each feature?

• A. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
• B. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
• C. Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
• D. Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline. Manually add the feature scores.

Answer: B

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

Answer: C

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)

Answer: B

A manufacturing company stores production volume data in a PostgreSQL database. The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge. Which solution will meet these requirements with the LEAST effort?

• A. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR duster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
• B. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
• C. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
• D. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

Answer: B