QUESTION 1
A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inferences. During times of peak usage, new instances launch before existing instances are fully ready. As a result, the model experiences inefficiencies and delays. Which solution will optimize the scaling process without affecting response times?

A. Change to a multi-model endpoint configuration in SageMaker Al.
B. Integrate Amazon API Gateway and AWS Lambda to manage invocations of the SageMaker AI inference endpoint.
C. Decrease the cooldown period for scale-in activities. Increase the maximum number of instances.
D. Increase the cooldown period after scale-out activities.

Correct Answer: D

QUESTION 2
An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features. Which solution will meet these requirements?

A. Apply L1 regularization to the training data. Retrain the model.
B. Use SageMaker Debugger to apply L1 regularization to the running model.
C. Increase the number of training iterations. Retrain the model.
D. Decrease the number of training iterations. Retrain the model.

Correct Answer: A

QUESTION 3
A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?

A. Amazon Elastic Container Registry (Amazon ECR)
B. Model packages from Amazon SageMaker Marketplace
C. Amazon SageMaker ML Lineage Tracking
D. Amazon SageMaker Model Registry

Correct Answer: D

QUESTION 4
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement?

A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity.
B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters.
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations.
D. Use the Amazon SageMaker SI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100.

Correct Answer: D

QUESTION 5
An ML engineer needs to process thousands of existing CSV documents and new CSV documents that are uploaded. The CSV documents 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?

A. 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.
B. 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.
C. 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.
D. 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.

Correct Answer: A

QUESTION 6
A company has an ML model in Amazon SageMaker Al. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features. Which solution will meet this requirement with the LEAST operational overhead?

A. Configure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the emit_metrics option is enabled in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in specific metrics that are related to data quality.
B. Configure SageMaker Model Monitor. Establish a model quality baseline. Ensure that the comparison_method option is set to Robust in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in model quality metrics.
C. Use SageMaker Debugger with custom rules to track shifts in feature distributions. Configure Amazon CloudWatch alarms to notify the company when the rules detect significant changes.
D. Use Amazon CloudWatch to directly observe the SageMaker Al endpoint’s performance metrics. Manually analyze the CloudWatch logs for indicators of data drift or shifts in feature distribution.

Correct Answer: A

QUESTION 7
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment. Which solution will meet this requirement?

A. Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
B. Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
C. Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers. Implement cross-entropy loss functions.
D. Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.

Correct Answer: B

QUESTION 8
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive. A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database. Which solution will meet these requirements with the LEAST implementation effort?

A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
B. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
C. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
D. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.

Correct Answer: A

QUESTION 9
A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker Al. The company has not hosted a model on SageMaker Al previously. An ML engineer needs to implement a solution to track model versions. The solution also must provide recommendations about which Amazon EC2 instance types to use to host the model. Which solution will meet these requirements?

A. Register the model in Amazon Elastic Container Registry (Amazon ECR). Use AWS Compute Optimizer for recommendations about instance types.
B. Register the model in the SageMaker Model Registry. Use SageMaker Autopilot for recommendations about instance types.
C. Register the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types.
D. Register the model in Amazon Elastic Container Registry (Amazon ECR). Use SageMaker Experiments for recommendations about instance types.

Correct Answer: C

QUESTION 10
A company is creating an ML model to identify defects in a product. The company has gathered a dataset and has stored the dataset in TIFF format in Amazon S3. The dataset contains 200 images in which the most common defects are visible. The dataset also contains 1,800 images in which there is no defect visible. An ML engineer trains the model and notices poor performance in some classes. The ML engineer identifies a class imbalance problem in the dataset. What should the ML engineer do to solve this problem?

A. Use a few hundred images and Amazon Rekognition Custom Labels to train a new model.
B. Undersample the 200 images in which the most common defects are visible.
C. Oversample the 200 images in which the most common defects are visible.
D. Use all 2,000 images and Amazon Rekognition Custom Labels to train a new model.

Correct Answer: C

QUESTION 11
A company has 12 ML models in production. Each model has its own model endpoint that was configured in Amazon SageMaker Studio. The company needs to optimize costs without affecting the availability of the model inferences. Which solution will meet these requirements?

A. Delete the resources in SageMaker Studio.
B. Configure a SageMaker Al multi-model endpoint.
C. Create an Amazon ElastiCache (Redis OSS) cache to store frequently accessed data
D. Configure Amazon Translate to change the instance type for the inference endpoints.

Correct Answer: B

QUESTION 12
A company wants to improve the sustainability of its ML operations. Which actions will reduce the energy usage and computational resources that are associated with the company’s training jobs? (Select TWO.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
B. Use Amazon SageMaker Ground Truth for data labeling
C. Deploy models by using AWS Lambda functions.
D. Use AWS Trainium instances for training.
E. Use PyTorch or TensorFlow with the distributed training option.

Correct Answer: AD

QUESTION 13
A recommendation model uses ML and calls an Amazon SageMaker Al endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic. Which solution will meet these requirements?

A. Configure auto scaling on the SageMaker Al endpoint.
B. Create a new SageMaker Al endpoint. Deploy the model to the new endpoint.
C. Use SageMaker Neo to optimize the model for inference.
D. Attach an Auto Scaling group to the SageMaker Al endpoint.

Correct Answer: A

QUESTION 14
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket. The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model. The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference. Which solution will meet these requirements?

A. Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.
B. Use Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
C. Use AWS Lake Formation. Apply tag-based control on the data
D. Use the Generate Data Insights function in SageMaker Data Wrangler.

Correct Answer: A

QUESTION 15
An ML engineer needs to organize a large set of text documents into topics. The ML engineer will not know what the topics are in advance. The ML engineer wants to use built-in algorithms or pre-trained models available through Amazon SageMaker Al to process the documents. Which solution will meet these requirements?

A. Use the BlazingText algorithm to identify the relevant text and to create a set of topics based on the documents.
B. Use the Sequence-to-Sequence algorithm to summarize the text and to create a set of topics based on the documents.
C. Use the Object2Vec algorithm to create embeddings and to create a set of topics based on the embeddings.
D. Use the Latent Dirichlet Allocation (LDA) algorithm to process the documents and to create a set of topics based on the documents.

Correct Answer: D

QUESTION 16
An ML engineer trained an ML model on Amazon SageMaker AI to detect automobile accidents from closed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. Which solution will improve the model’s accuracy in the LEAST amount of time?

A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

Correct Answer: B

QUESTION 17
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?

A. Introduce early stopping.
B. Increase the size of the test set.
C. Increase the learning rate.
D. Decrease the learning rate.

Correct Answer: D

QUESTION 18
An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scaling with a target tracking scaling policy set to 100 invocations per model per minute. The SageMaker AI endpoint scales appropriately during normal business hours. However, the ML engineer notices that at the start of each business day, there are zero instances available to handle requests, which causes delays in processing. The ML engineer must ensure that the SageMaker Al endpoint can handle incoming requests at the start of each business day. Which solution will meet this requirement?

A. Reduce the SageMaker Al auto scaling cooldown period to the minimum supported value. Add an auto scaling lifecycle hook to scale the SageMaker Al instances.
B. Change the target metric to CPU utilization.
C. Modify the scaling policy target value to one.
D. Apply a step scaling policy that scales based on an Amazon CloudWatch alarm. Apply a second CloudWatch alarm and scaling policy to scale the minimum number of instances from zero to one at the start of each business day.

Correct Answer: D

QUESTION 19
A credit card company has a fraud detection model in production on an Amazon SageMaker Al 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 20
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker Al endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::Model
B. AWS::SageMaker::Endpoint
C. AWS::SageMaker::NotebookInstance
D. AWS::SageMaker::Pipeline

Correct Answer: A