Amazon MLA-C01 Question Answers

Question # 31

A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames. The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time. Which solution will meet these requirements?

A. Use SageMaker Ground Truth to annotate the video frames. 
B. Use SageMaker JumpStart to use pre-trained computer vision models to develop a labeling model.
C. Use SageMaker Data Wrangler to create a data workflow. Use the workflow to optimize the labeling process. 
D. Use the labeling interface of Amazon Augmented AI (Amazon A2I) with Amazon Rekognition to label the video frames. 

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Question # 32

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. Increase the learning rate. 
B. Remove some irrelevant features from the training dataset. 
C. Increase the value of the max_depth hyperparameter. 
D. Decrease the value of the max_depth hyperparameter. 

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Question # 33

A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?

A. F1 score 
B. Area Under the ROC Curve (AUC) 
C. Precision 
D. Recall 

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Question # 34

A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents. Which solution will meet this requirement with the LEAST operational effort? 

A. Use Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions. 
B. Use Amazon SageMaker AI to customize an AI model to monitor driver behavior and identify distractions. 
C. Integrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions. 
D. Use Amazon Comprehend to analyze text-based driver feedback and identify distractions. 

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Question # 35

An ML engineer develops a neural network model to predict whether customers will continue to subscribe to a service. The model performs well on training data. However, the accuracy of the model decreases significantly on evaluation data. The ML engineer must resolve the model performance issue. Which solution will meet this requirement?

A. Penalize large weights by using L1 or L2 regularization. 
B. Remove dropout layers from the neural network. 
C. Train the model for longer by increasing the number of epochs.
 D. Capture complex patterns by increasing the number of layers. 

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Question # 36

A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store. The company needs a hybrid system to make the shared data store accessible to onpremises servers and Amazon SageMaker AI notebooks that will consume the data. File locking is required for the data producers. Which AWS storage solution will meet these requirements?

A. Use an Amazon S3 bucket to store the data. Use Mountpoint for Amazon S3 to mount the S3 bucket to the on-premises servers and the SageMaker AI notebooks. 
B. Use an Amazon Elastic File System (Amazon EFS) file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks. 
C. Use an Amazon FSx for Lustre file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks. 
D. Use an Amazon Elastic Block Store (Amazon EBS) volume to store the data. Mount the volume to the on-premises servers and the SageMaker AI notebooks. 

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Question # 37

A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day. Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities. Which solution will meet these requirements?

A. Schedule an Amazon SageMaker batch transform job by using AWS Lambda. 
B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling. 
C. Use Amazon SageMaker Serverless Inference with provisioned concurrency. 
D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

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Question # 38

An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset. The ML engineer must improve the model's performance. Which solution will meet this requirement?

A. Optimize for accuracy. Use image augmentation on the less common images to generate new samples. 
B. Optimize for F1 score. Use image augmentation on the less common images to generate new samples. 
C. Optimize for accuracy. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples.
 D. Optimize for F1 score. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples. 

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Question # 39

A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds. The company needs sub-second latency for a real-time OpenSearch dashboard. Which architectural change will meet this requirement?

A. Use zero buffering in the Firehose stream and tune the PutRecordBatch batch size. 
B. Replace Firehose with AWS DataSync and enhanced fan-out consumers. 
C. Increase the Firehose buffer interval to 120 seconds. 
D. Replace Firehose with Amazon SQS. 

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Question # 40

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random. Which solution will meet these requirements?

A. Increase the temperature parameter and the top_k parameter. 
B. Increase the temperature parameter. Decrease the top_k parameter. 
C. Decrease the temperature parameter. Increase the top_k parameter.
 D. Decrease the temperature parameter and the top_k parameter. 

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