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

Amazon MLA-C01 Practice Test - Questions Answers

Add to Whishlist

List of questions

Question 1

Report Export Collapse

An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.

What should the ML engineer do to improve the training process?

Introduce early stopping.

Introduce early stopping.

Increase the size of the test set.

Increase the size of the test set.

Increase the learning rate.

Increase the learning rate.

Decrease the learning rate.

Decrease the learning rate.

Suggested answer: D
asked 27/05/2025
Ibrahim Isaaq
36 questions

Question 2

Report Export Collapse

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.

Which solution will meet these requirements with the LEAST operational overhead?

Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.

Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.

Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.

Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.

Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.

Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.

Suggested answer: A
asked 27/05/2025
Ricardo Rodrigues
31 questions

Question 3

Report Export Collapse

Case Study

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 needs to use the central model registry to manage different versions of models in the application.

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

Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.

Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.

Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Use the SageMaker Model Registry and model groups to catalogthe models.

Use the SageMaker Model Registry and model groups to catalogthe models.

Use the SageMaker Model Registry and unique tags for each model version.

Use the SageMaker Model Registry and unique tags for each model version.

Suggested answer: C
asked 27/05/2025
Mihir Mishra
43 questions

Question 4

Report Export Collapse

Case Study

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?

Use Managed Spot Training.

Use Managed Spot Training.

Use SageMaker managed warm pools.

Use SageMaker managed warm pools.

Use SageMaker Training Compiler.

Use SageMaker Training Compiler.

Use the SageMaker distributed data parallelism (SMDDP) library.

Use the SageMaker distributed data parallelism (SMDDP) library.

Suggested answer: B
asked 27/05/2025
Nezha El Fakraoui
37 questions

Question 5

Report Export Collapse

Case Study

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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

Use SageMaker Experiments to facilitate the approval process during model registration.

Use SageMaker Experiments to facilitate the approval process during model registration.

Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to 'Approved.'

Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to 'Approved.'

Suggested answer: D
asked 27/05/2025
Daniela Const
49 questions

Question 6

Report Export Collapse

Case Study

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 needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.

Which action will meet this requirement?

Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.

Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.

Use AWS Glue Data Quality to monitor bias.

Use AWS Glue Data Quality to monitor bias.

Use SageMaker notebooks to compare the bias.

Use SageMaker notebooks to compare the bias.

Suggested answer: A
asked 27/05/2025
rashid Elamin
39 questions

Question 7

Report Export Collapse

HOTSPOT

A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML

engineer needs to prepare and store the data so that the company can use the data to train ML models.

Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)

* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.

* Store the resulting data back in Amazon S3.

* Use Amazon Athena to infer the schemas and available columns.

* Use AWS Glue crawlers to infer the schemas and available columns.

* Use AWS Glue DataBrew for data cleaning and feature engineering.


Amazon MLA-C01 image Question 7 148934 05272025071351000
Correct answer: Amazon MLA-C01 image answer Question 7 148934 05272025071351000
asked 27/05/2025
Michael Whitehouse
58 questions

Question 8

Report Export Collapse

HOTSPOT

An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.

Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)

* Access the store to build datasets for training.

* Create a feature group.

* Ingest the records.


Amazon MLA-C01 image Question 8 148935 05272025071351000
Correct answer: Amazon MLA-C01 image answer Question 8 148935 05272025071351000
asked 27/05/2025
Aamer Shahzad
38 questions

Question 9

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.

Which AWS service or feature can aggregate the data from the various data sources?

Amazon EMR Spark jobs

Amazon EMR Spark jobs

Amazon Kinesis Data Streams

Amazon Kinesis Data Streams

Amazon DynamoDB

Amazon DynamoDB

AWS Lake Formation

AWS Lake Formation

Suggested answer: A
asked 27/05/2025
Stefano Humphries
47 questions

Question 10

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.

After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

Which solution will meet these requirements?

Use Amazon Athena to automatically detect the anomalies and to visualize the result.

Use Amazon Athena to automatically detect the anomalies and to visualize the result.

Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Suggested answer: C
asked 27/05/2025
chanon witchajutakul
31 questions
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?