Free MLA-C01 Exam Dumps Preview

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

An ML engineer must ensure that all data is encrypted in transit during the execution of an ML training job.
This includes applying encryption in transit to all processes utilized by Amazon SageMaker throughout the training job.

Which solution will effectively fulfill these requirements?

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.

Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A company intends to deploy a machine learning model for production inference on an Amazon SageMaker endpoint. The inference payload size will range from 100 MB to 300 MB, and each inference request must be processed within 60 minutes or less.

Which Amazon SageMaker inference option would best fulfill these requirements?

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.

Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts. An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.

Which solution will meet these requirements with the LEAST development effort?

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups. The company uses a single AWS account and stores all the

training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.

Which solution will provide the ML engineers with the appropriate access?

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 company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.

Which solution will meet these requirements?

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.

How can the company MINIMIZE infrastructure startup times for these jobs?

A company is developing a real-time data processing pipeline for an e-commerce application that generates large volumes of clickstream data. The data needs to be ingested, processed, and visualized in near real-time. Additionally, the solution must support SQL for data processing and Jupyter notebooks for interactive analysis.

Which solution would best meet these requirements?

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 compute costs reach a specific threshold.

Which solution will meet these requirements?

A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.

Which solution will meet these requirements?

A company employs Amazon SageMaker for its machine learning processes. During a compliance audit, it is discovered that an Amazon S3 bucket containing training data is encrypted using server-side encryption with S3 managed keys (SSE-S3). The company requires the use of customer managed keys instead.

In response, an ML engineer updates the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS) without making any other configuration changes. However, following this update, SageMaker training jobs begin to fail with AccessDenied errors.

What steps should the ML engineer take to resolve this issue?

A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.

Which modeling approach should the company use to meet this requirement?

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score. During a baseline analysis of model quality, the company recorded a threshold for the F1 score.
After several months of no change, the model's F1 score decreases signi cantly.

What could be the reason for the reduced F1 score?

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable.

Which solution will provide this information with the LEAST development effort?

An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.

Which metric nding should the ML engineer prioritize the MOST when choosing the model?