ExamGecko
Login
Home / Amazon / MLA-C01 / List of questions
Ask Question

Amazon MLA-C01 Practice Test - Questions Answers, Page 2

Add to Whishlist

List of questions

Question 11

Report Export Collapse

Case study

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 training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

Use AWS Glue to transform the categorical data into numerical data.

Use AWS Glue to transform the categorical data into numerical data.

Use AWS Glue to transform the numerical data into categorical data.

Use AWS Glue to transform the numerical data into categorical data.

Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Suggested answer: C
asked 27/05/2025
mark linde
39 questions

Question 12

Report Export Collapse

Case study

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?

Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

Use AWS Glue DataBrew built-in features to oversample the minority class.

Use AWS Glue DataBrew built-in features to oversample the minority class.

Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Suggested answer: D
asked 27/05/2025
Alejandro Ramirez Cuesta
42 questions

Question 13

Report Export Collapse

Case study

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?

LightGBM

LightGBM

Linear learner

Linear learner

-means clustering

-means clustering

Neural Topic Model (NTM)

Neural Topic Model (NTM)

Suggested answer: B
asked 27/05/2025
Nicola Grossi
48 questions

Question 14

Report Export Collapse

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 significantly.

What could be the reason for the reduced F1 score?

Concept drift occurred in the underlying customer data that was used for predictions.

Concept drift occurred in the underlying customer data that was used for predictions.

The model was not sufficiently complex to capture all the patterns in the original baseline data.

The model was not sufficiently complex to capture all the patterns in the original baseline data.

The original baseline data had a data quality issue of missing values.

The original baseline data had a data quality issue of missing values.

Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Suggested answer: A
asked 27/05/2025
AJ Foraker
45 questions

Question 15

Report Export Collapse

A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.

The company needs to centralize management of the team's permissions.

Which solution will meet this requirement?

Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.

Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.

Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.

Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.

Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.

Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.

Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

Suggested answer: A
asked 27/05/2025
Naeem Navaid Shaikh
47 questions

Question 16

Report Export Collapse

An ML engineer needs to use an ML model to predict the price of apartments in a specific location.

Which metric should the ML engineer use to evaluate the model's performance?

Accuracy

Accuracy

Area Under the ROC Curve (AUC)

Area Under the ROC Curve (AUC)

F1 score

F1 score

Mean absolute error (MAE)

Mean absolute error (MAE)

Suggested answer: D
asked 27/05/2025
Danilo Romelli
66 questions

Question 17

Report Export Collapse

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?

Become a Premium Member for full access
  Unlock Premium Member

Question 18

Report Export Collapse

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.

Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

Become a Premium Member for full access
  Unlock Premium Member

Question 19

Report Export Collapse

An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account in the same Region.

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

Become a Premium Member for full access
  Unlock Premium Member

Question 20

Report Export Collapse

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.

Which solutions will mitigate this problem? (Choose two.)

Become a Premium Member for full access
  Unlock Premium Member
Total 85 questions
Go to page: of 9
Search
Open VPLUS File
Convert VPLUS to PDF

Related questions

A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model. Which solution will set up the required online validation with the LEAST operational overhead?
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain. Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)
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 needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements?
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?
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements?
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements. Which metric should the ML engineer use for the model recalibration?
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions. What should the ML engineer do to improve the fraud detection for new transactions?
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket. Which solution will meet these requirements with the LEAST operational effort?