MLS-C01 Online Practice Questions and Answers

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not.

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

A. Seq2seq

B. XGBoost

C. K-means

D. Random Cut Forest (RCF)

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.

How should the Specialist frame this business problem?

A. Streaming classification

B. Binary classification

C. Multi-category classification

D. Regression classification

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data

available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations

appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A. Drop all records from the dataset where age has been set to 0.

B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C. Drop the age feature from the dataset and train the model using the rest of the features.

D. Use k-means clustering to handle missing features.

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A. Linear regression is inappropriate. The residuals do not have constant variance.

B. Linear regression is inappropriate. The underlying data has outliers.

C. Linear regression is appropriate. The residuals have a zero mean.

D. Linear regression is appropriate. The residuals have constant variance.

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.

What should the company do to reduce the processing time of loan applications?

A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.

B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.

C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.

D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the

customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.

Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then

"Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product."

The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.

Which functionality of Amazon Comprehend should the team use to meet these requirements?

A. Custom classification with multi-class mode

B. Custom classification with multi-label mode

C. Custom entity recognition

D. Built-in models

A company is building a machine learning (ML) model to classify images of plants. An ML specialist has trained the model using the Amazon SageMaker built-in Image Classification algorithm. The model is hosted using a SageMaker endpoint on an ml.m5.xlarge instance for real-time inference. When used by researchers in the field, the inference has greater latency than is acceptable. The latency gets worse when multiple researchers perform inference at the same time on their devices. Using Amazon CloudWatch metrics, the ML specialist notices that the ModelLatency metric shows a high value and is responsible for most of the response latency.

The ML specialist needs to fix the performance issue so that researchers can experience less latency when performing inference from their devices.

Which action should the ML specialist take to meet this requirement?

A. Change the endpoint instance to an ml.t3 burstable instance with the same vCPU number as the ml.m5.xlarge instance has.

B. Attach an Amazon Elastic Inference ml.eia2.medium accelerator to the endpoint instance.

C. Enable Amazon SageMaker Autopilot to automatically tune performance of the model.

D. Change the endpoint instance to use a memory optimized ML instance.

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.

How should the company prepare the data for the model to improve the model's accuracy?

A. Adjust the class weight to account for each machine type.

B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

C. Undersample the non-failure events. Stratify the non-failure events by machine type.

D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.

Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Use SageMaker Clarify to automatically detect data bias

B. Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.

C. Use SageMaker Model Monitor to generate a bias drift report.

D. Configure SageMaker Data Wrangler to generate a bias report.

E. Use SageMaker Experiments to perform a data check

A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model.

Which approaches will meet this requirement? (SELECT TWO )

A. Replace On-Demand Instances with Spot Instances

B. Configure model auto scaling dynamically to adjust the number of instances automatically.

C. Replace CPU-based EC2 instances with GPU-based EC2 instances.

D. Use multiple training instances.

E. Use a pre-trained version of the model. Run incremental training.