MLS-C01 Question Bank · Latest 50 Questions Free Preview (Page 3/5)

A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question. A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model. Which solution will meet these requirements?

• A. Perform one-hot encoding on every possible option for each question of the survey.
• B. Perform binning on all the answers each respondent selected for each question.
• C. Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.
• D. Use Amazon Textract to create numeric features for each set of possible responses.

Answer: A

A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN). The company developed a similar model previously but trained the model to classify a different set of objects. The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects. Which combination of steps will accomplish this goal with the LEAST amount of effort? (Choose two.)

• A. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
• B. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
• C. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.
• D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
• E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.

Answer: D, E

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Choose two.)

• A. InferenceLatency
• B. Mean squared error (MSE)
• C. Root mean squared error (RMSE)
• D. Precision
• E. Accuracy

Answer: B, C

A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power for a dataset feature has a score of 1. What is the cause of the score?

• A. Target leakage occurred in the imported dataset.
• B. The data scientist did not fine-tune the training and validation split.
• C. The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power.
• D. The data scientist did not process the features enough to accurately calculate prediction power.

Answer: A

A company’s machine learning (ML) team needs to build a system that can detect whether people in a collection of images are wearing the company’s logo. The company has a set of labeled training data. Which algorithm should the ML team use to meet this requirement?

• A. Principal component analysis (PCA)
• B. Recurrent neural network (RNN)
• C. К-nearest neighbors (k-NN)
• D. Convolutional neural network (CNN)

Answer: D

A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse. A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse. Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.)

• A. Define the feature variables and target variable for the churn prediction model.
• B. Use the SOL EXPLAIN_MODEL function to run predictions.
• C. Write a CREATE MODEL SQL statement to create a model.
• D. Use Amazon Redshift Spectrum to train the model.
• E. Manually export the training data to Amazon S3.
• F. Use the SQL prediction function to run predictions.

Answer: A, C, F

A company is building a predictive maintenance system using real-time data from devices on remote sites. There is no AWS Direct Connect connection or VPN connection between the sites and the company's VPC. The data needs to be ingested in real time from the devices into Amazon S3. Transformation is needed to convert the raw data into clean .csv data to be fed into the machine learning (ML) model. The transformation needs to happen during the ingestion process. When transformation fails, the records need to be stored in a specific location in Amazon S3 for human review. The raw data before transformation also needs to be stored in Amazon S3. How should an ML specialist architect the solution to meet these requirements with the LEAST effort?

• A. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose.
• B. Use Amazon Managed Streaming for Apache Kafka. Set up workers in Amazon Elastic Container Service (Amazon ECS) to move data from Kafka brokers to Amazon S3 while transforming it. Configure workers to store raw and unsuccessfully transformed data in different S3 buckets.
• C. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an Apache Spark job in AWS Glue for data transformation. Enable source record backup and configure the error prefix.
• D. Use Amazon Kinesis Data Streams in front of Amazon Data Firehose. Use Kinesis Data Streams with AWS Lambda to store raw data in Amazon S3. Configure Firehose to invoke a Lambda function for data transformation with Amazon S3 as the destination.

Answer: A

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.

Answer: B

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.

Answer: C

A machine learning (ML) engineer is creating a binary classification model. The ML engineer will use the model in a highly sensitive environment. There is no cost associated with missing a positive label. However, the cost of making a false positive inference is extremely high. What is the most important metric to optimize the model for in this scenario?

• A. Accuracy
• B. Precision
• C. Recall
• D. F1

Answer: B