Free Amazon MLA-C01 Exam Dumps Questions & Answers

A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?

• A.  Use the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm in Amazon SageMaker AI.
• B.  Use the Random Cut Forest (RCF) algorithm in Amazon SageMaker AI.
• C.  Use the k-means algorithm in Amazon SageMaker AI.
• D.  Use the principal component analysis (PCA) algorithm in Amazon SageMaker AI.

A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.
Which solution will meet these requirements?

• A.  Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
• B.  Add resource tagging by editing each user ' s IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
• C.  Add resource tagging by editing each user ' s IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
• D.  Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company ' s main competitor.
Which solution will meet this requirement?

• A.  Configure an Amazon Q Business retriever to exclude the competitor's name.
• B.  Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor ' s name.
• C.  Configure document attribute boosting in Amazon Q Business to deprioritize the competitor ' s name.
• D.  Configure the competitor ' s name as a blocked phrase in Amazon Q Business.

A healthcare analytics company wants to segment patients into groups that have similar risk factors to develop personalized treatment plans. The company has a dataset that includes patient health records, medication history, and lifestyle changes. The company must identify the appropriate algorithm to determine the number of groups by using hyperparameters.
Which solution will meet these requirements?

• A.  Use the Amazon SageMaker k-means clustering algorithm. Set k to specify the number of clusters.
• B.  Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to control tree complexity for risk groups.
• C.  Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set a contamination hyperparameter for risk anomaly detection.
• D.  Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to determine the number of training iterations for risk groups.

An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scaling with a target tracking scaling policy set to 100 invocations per model per minute. The SageMaker AI endpoint scales appropriately during normal business hours. However, the ML engineer notices that at the start of each business day, there are zero instances available to handle requests, which causes delays in processing.
The ML engineer must ensure that the SageMaker AI endpoint can handle incoming requests at the start of each business day.
Which solution will meet this requirement?

• A.  Apply a step scaling policy that scales based on an Amazon CloudWatch alarm. Apply a second CloudWatch alarm and scaling policy to scale the minimum number of instances from zero to one at the start of each business day.
• B.  Reduce the SageMaker AI auto scaling cooldown period to the minimum supported value. Add an auto scaling lifecycle hook to scale the SageMaker AI instances.
• C.  Modify the scaling policy target value to one.
• D.  Change the target metric to CPU utilization.