MLS-C01 Question Bank · Latest 50 Questions Free Preview (Page 1/5)

A company that operates oil platforms uses drones to photograph locations on oil platforms that are difficult for humans to access to search for corrosion. Experienced engineers review the photos to determine the severity of corrosion. There can be several corroded areas in a single photo. The engineers determine whether the identified corrosion needs to be fixed immediately, scheduled for future maintenance, or requires no action. The corrosion appears in an average of 0.1% of all photos. A data science team needs to create a solution that automates the process of reviewing the photos and classifying the need for maintenance. Which combination of steps will meet these requirements? (Choose three.)

• A. Use an object detection algorithm to train a model to identify corrosion areas of a photo.
• B. Use Amazon Rekognition with label detection on the photos.
• C. Use a k-means clustering algorithm to train a model to classify the severity of corrosion in a photo.
• D. Use an XGBoost algorithm to train a model to classify the severity of corrosion in a photo.
• E. Perform image augmentation on photos that contain corrosion.
• F. Perform image augmentation on photos that do not contain corrosion.

Answer: A, D, E

A data scientist is implementing a deep learning neural network model for an object detection task on images. The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time. The data scientist must ensure that jobs that underperform are stopped. The data scientist must allocate computational resources to well-performing hyperparameter configurations. The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size. Which technique will meet these requirements with LEAST computational time?

• A. Grid search
• B. Random search
• C. Bayesian optimization
• D. Hyperband

Answer: D

A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR). A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model. Which solution will meet these requirements MOST cost-effectively?

• A. Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances.
• B. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances.
• C. Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances.
• D. Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.

Answer: B

A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures. The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page. Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

• A. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
• B. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
• C. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
• D. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

Answer: A

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files. The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset. Which approach will meet these requirements with the LEAST development effort?

• A. Impute the missing values by using the linear regression method. Use the entire dataset and the imputed values to train the DeepAR model.
• B. Replace the missing values with not a number (NaN). Use the entire dataset and the encoded missing values to train the DeepAR model.
• C. Impute the missing values by using a forward fill. Use the entire dataset and the imputed values to train the DeepAR model.
• D. Impute the missing values by using the mean value. Use the entire dataset and the imputed values to train the DeepAR model.

Answer: B

An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users. Which solution will meet these requirements with the LEAST operational overhead?

• A. Create a new SageMaker endpoint for the new model. Configure an Application Load Balancer (ALB) to distribute traffic between the old model and the new model.
• B. Modify the existing endpoint to use SageMaker production variants to distribute traffic between the old model and the new model.
• C. Modify the existing endpoint to use SageMaker batch transform to distribute traffic between the old model and the new model.
• D. Create a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB) to distribute traffic between the old model and the new model.

Answer: B

A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions. Which solution will meet these requirements?

• A. Amazon S3 with S3 Cross-Region Replication (CRR)
• B. Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region
• C. Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability
• D. AWS Storage Gateway Volume Gateway

Answer: A

A news company is developing an article search tool for its editors. The search tool should look for the articles that are most relevant and representative for particular words that are queried among a corpus of historical news documents. The editors test the first version of the tool and report that the tool seems to look for word matches in general. The editors have to spend additional time to filter the results to look for the articles where the queried words are most important. A group of data scientists must redesign the tool so that it isolates the most frequently used words in a document. The tool also must capture the relevance and importance of words for each document in the corpus. Which solution meets these requirements?

• A. Extract the topics from each article by using Latent Dirichlet Allocation (LDA) topic modeling. Create a topic table by assigning the sum of the topic counts as a score for each word in the articles. Configure the tool to retrieve the articles where this topic count score is higher for the queried words.
• B. Build a term frequency for each word in the articles that is weighted with the article's length. Build an inverse document frequency for each word that is weighted with all articles in the corpus. Define a final highlight score as the product of both of these frequencies. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.
• C. Download a pretrained word-embedding lookup table. Create a titles-embedding table by averaging the title's word embedding for each article in the corpus. Define a highlight score for each word as inversely proportional to the distance between its embedding and the title embedding. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.
• D. Build a term frequency score table for each word in each article of the corpus. Assign a score of zero to all stop words. For any other words, assign a score as the word’s frequency in the article. Configure the tool to retrieve the articles where this frequency score is higher for the queried words.

Answer: B

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours. The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case. How should the developer verify the suitability of an ARIMA approach?

• A. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.
• B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
• C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
• D. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

Answer: C

An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset. Which approach should the ML specialist use to improve the performance of the model on the testing data?

• A. Increase the value of the momentum hyperparameter.
• B. Reduce the value of the dropout_rate hyperparameter.
• C. Reduce the value of the learning_rate hyperparameter
• D. Increase the value of the L2 hyperparameter.

Answer: D