10 free sample questions from a bank of 106, with the correct answers and explanations. No signup required — start practising right now.
1Case 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.
Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
Use the SageMaker Model Registry and model groups to catalog the models.
Use the SageMaker Model Registry and unique tags for each model version.
Answer: C
2Case 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 Kinesis Data Streams
Amazon DynamoDB
AWS Lake Formation
Answer: D
3A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?
Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
Answer: B
4A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files.
Which solution will meet these requirements with the LEAST operational overhead?
Create a pipeline in Amazon SageMaker Pipelines to generate a new model. Call the new model from Amazon Bedrock to perform RAG queries.
Convert the data into vectors. Store the data in an Amazon Neptune database. Connect the database to Amazon Bedrock. Call the Amazon Bedrock API to perform RAG queries.
Fine-tune an existing LLM by using an AutoML job in Amazon SageMaker. Configure the S3 bucket as a data source for the AutoML job. Deploy the LLM to a SageMaker endpoint. Use the endpoint to perform RAG queries.
Create a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries.
Answer: D
5A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less.
Which SageMaker inference option will meet these requirements?
Serverless inference
Asynchronous inference
Real-time inference
Batch transform
Answer: B
6An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?
Reduce the size of the dataset.
Transform some of the images in the dataset.
Apply random oversampling on the dataset.
Apply random data splitting on the dataset.
Answer: C
7A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained.
Which solution will meet this requirement with the LEAST development effort?
Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.
Create Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.
Answer: B
8A company needs to extract entities from a PDF document to build a classifier model.
Which solution will extract and store the entities in the LEAST amount of time?
Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.
Answer: C
9A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks.
Which solution will meet these requirements?
Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.
Set up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
Set up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
Set up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.
Answer: A
10Case 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 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 AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.