MLA-C01 Amazon-Web-Services Exam Questions and Free Practice Test

Question 13

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

A. Process data at each step by using Amazon SageMaker Data Wrangle

B. Automate the process by using Data Wrangler jobs.

C. Use Amazon SageMaker notebooks for each data processing ste

D. Automate the process by using Amazon EventBridge.

E. Process data at each step by using AWS Lambda function

F. Automate the process by using AWS Step Functions and Amazon EventBridge.

G. Use Amazon SageMaker Pipelines to create a pipeline of data processing step

H. Automate the pipeline by using Amazon EventBridge.

Correct Answer:D

Question 14

A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

A. Set up SageMaker Debugger and create a custom rule.

B. Set up blue/green deployments with all-at-once traffic shifting.

C. Set up blue/green deployments with canary traffic shifting.

D. Set up shadow testing with a shadow variant of the new model.

Correct Answer:D

Question 15

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 the competitor's name as a blocked phrase in Amazon Q Business.

B. Configure an Amazon Q Business retriever to exclude the competitor??s name.

C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Correct Answer:A

Question 16

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. Create code to evaluate each instance's memory and compute usage.

B. Add cost allocation tags to the resource

C. Activate the tags in AWS Billing and Cost Management.

D. Check AWS CloudTrail event history for the creation of the resources.

E. Run AWS Compute Optimizer.

Correct Answer:D

Question 17

A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

A. Download the file to a local workstatio

B. Perform one-hot encoding by using a custom Python script.

C. Create an Apache Spark job that uses a custom processing script on Amazon EMR.

D. Create a SageMaker processing job by calling the SageMaker Python SDK.

E. Create a data flow in SageMaker Data Wrangle

F. Configure a transform step.

Correct Answer:D

Question 18

A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

A. Create a custom tag for each of the three categorie

B. Add the tags to the model packages in the SageMaker Model Registry.

C. Create a model group for each categor

D. Move the existing models into these category model groups.

E. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.

F. Create a Model Registry collection for each of the three categorie

G. Move the existing model groups into the collections.

Correct Answer:A

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