Free AWS Certified Machine Learning Engineer – Associate Exam MLA-C01 Exam Practice Test

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?

• Set up SageMaker Debugger and create a custom rule.
• Set up blue/green deployments with all-at-once traffic shifting.
• Set up blue/green deployments with canary traffic shifting.
• Set up shadow testing with a shadow variant of the new model.

Case studyAn 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
• Linear learner
• К-means clustering
• Neural Topic Model (NTM)

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.)

• Enable early stopping on the model.
• Increase dropout in the layers.
• Increase the number of layers.
• Increase the number of neurons.
• Investigate and reduce the sources of model bias.

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?

• Accuracy
• Precision
• Recall
• Specificity

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 theexisting 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?

• Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
• Create a model group for each category. Move the existing models into these category model groups.
• Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
• Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 ТВ of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.Which solution will meet these requirements?

• Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
• Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
• Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
• Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.Which solution will meet these requirements?

• Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
• Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
• Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
• Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

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?

• Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
• Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
• Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint. Monitor the number of invocations by using Amazon CloudWatch.
• Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.

An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similardataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems. The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.Which solution will meet these requirements with the LEAST operational overhead?

• Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
• Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
• Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
• Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.Which metric finding should the ML engineer prioritize the MOST when choosing the model?

• Low precision
• High precision
• Low recall
• High recall