leads4pass shares the latest valid MLS-C01 dumps that meet the requirements for passing the AWS Certified Machine Learning – Specialty (MLS-C01) certification exam!
leads4pass MLS-C01 dumps provide two learning solutions, PDF and VCE, to help candidates experience real simulated exam scenarios! Now! Get the latest leads4pass MLS-C01 dumps with PDF and VCE:
https://www.leads4pass.com/aws-certified-machine-learning-specialty.html (302 Q&A)
| From | Exam name | Free share | Last updated |
| leads4pass | AWS Certified Machine Learning – Specialty (MLS-C01) | Q14-Q28 | MLS-C01 dumps (Q1-Q13) |
New Q14:
A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstancesetting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.
Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstancesetting?
A. 10
B. 30
C. 600
D. 2,400
Correct Answer: C
SageMakerVariantInvocationsPerInstance = (MAX_RPS * SAFETY_FACTOR) * 60 AWS recommended Saf_fac =0 .5
https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-scaling-loadtest.html
New Q15:
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable
What should be done to reduce the impact of having such a large number of features?
A. Perform one-hot encoding on highly correlated features
B. Use matrix multiplication on highly correlated features.
C. Create a new feature space using principal component analysis (PCA)
D. Apply the Pearson correlation coefficient
Correct Answer: C
New Q16:
A machine learning (ML) specialist is developing a deep learning sentiment analysis model that is based on data from movie reviews. After the ML specialist trains the model and reviews the model results on the validation set, the ML specialist discovers that the model is overfitting.
Which solutions will MOST improve the model generalization and reduce overfitting? (Choose three.)
A. Shuffle the dataset with a different seed.
B. Decrease the learning rate.
C. Increase the number of layers in the network.
D. Add L1 regularization and L2 regularization.
E. Add dropout.
F. Decrease the number of layers in the network.
Correct Answer: DEF
A: possible but unlikely for movie reviews
B: wrong https://www.google.com/url?sa=tandrct=jandq=andesrc=sandsource=webandcd=andcad=rjaanduact=8andved=2ahUKEwi31N_10eX9AhWYQ0EAHXDFCAwQFnoECA8QAwandurl=https%3A%2F%2Fdeepchecks.com%2Fquestion%2Fdoes-learningrate-affect-overfitting%2Fandusg=AOvVaw19RT-u_XyEe8FG_10R6aFC
C: wrong because would increase complexity and potentially overfit
D: correct
E: Correct
F: correct
New Q17:
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?
A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
B. Run the Model Monitor baseline job again on the new training set. Configure the Model Monitor to use the new baseline.
C. Delete the endpoint and recreate it with the original configuration.
D. Retrain the model again by using a combination of the original training set and the new training set.
Correct Answer: B
https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-create-baseline.html
New Q18:
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied
transfer learning on a neural network that was pre-trained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
1.
68% accuracy on the training set
2.
67% accuracy on the validation set
What can the machine learning specialist do to improve the system\’s accuracy?
A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model\’s hyperparameters.
B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C. Use a neural network model with more layers that are pre-trained on ImageNet and apply transfer learning to increase the variance.
D. Train a new model using the current neural network architecture.
Correct Answer: B
New Q19:
A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup.
Which solution will meet these requirements?
A. Use File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.
B. Create an Amazon FSx for the Lustre file system. Link the file system to the S3 buckets.
C. Create an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the training instances.
D. Use FastFile mode in SageMaker to stream the files on demand from the S3 buckets.
Correct Answer: D
For larger datasets with larger files (more than 50 MB per file), the first option is to try fast file mode, which is more straightforward to use than FSx for Lustre because it doesn’t require creating a file system or connecting to a VPC. Fast file mode is ideal for large file containers (more than 150 MB), and might also do well with files more than 50 MB.
New Q20:
A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%.
What should the specialist consider to fix this issue?
A. A longer training time
B. Making the network larger
C. Using a different optimizer
D. Using some form of regularization
Correct Answer: D
New Q21:
A medical imaging company wants to train a computer vision model to detect areas of concern in patients\’ CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?
A. Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
B. Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
C. Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
D. Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
Correct Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html
New Q22:
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
A. Recall
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)
Correct Answer: D
https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/cross-validation.html
Area Under the ROC Curve (AUC) is a commonly used metric to compare and evaluate machine learning classification models against each other. The AUC measures the model\’s ability to distinguish between positive and negative classes and its performance across different classification thresholds. The AUC ranges from 0 to 1, with a score of 1 representing a perfect classifier and a score of 0.5 representing a classifier that is no better than random.
While recall is an important evaluation metric for classification models, it alone is not sufficient to compare and evaluate different models against each other. Recall measures the proportion of actual positive cases that are correctly identified as positive, but does not take into account the false positive rate.
New Q23:
A bank wants to launch a low-rate credit promotion campaign. The bank must identify which customers to target with the promotion and wants to make sure that each customer\’s full credit history is considered when an approval or denial decision is made.
The bank\’s data science team used the XGBoost algorithm to train a classification model based on account transaction features. The data science team deployed the model by using the Amazon SageMaker model hosting service. The accuracy of the model is sufficient, but the data science team wants to be able to explain why the model denies the promotion to some customers.
What should the data science team do to meet this requirement in the MOST operationally efficient manner?
A. Create a SageMaker notebook instance. Upload the model artifact to the notebook. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart for the individual predictions.
B. Retrain the model by using SageMaker Debugger. Configure Debugger to calculate and collect Shapley values. Create a chart that shows features and SHapley. Additive explanations (SHAP) values to explain how the features affect the model outcomes.
C. Set up and run an explainability job powered by SageMaker Clarify to analyze the individual customer data, using the training data as a baseline. Create a chart that shows features and Shapley Additive explanations (SHAP) values to explain how the features affect the model outcomes.
D. Use SageMaker Model Monitor to create Shapley values that help explain model behavior. Store the Shapley values in Amazon S3. Create a chart that shows features and Shapley Additive explanations (SHAP) values to explain how the features affect the model outcomes.
Correct Answer: C
New Q24:
An e-commerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)
A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
B. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
C. Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
D. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
E. Use Amazon Rekognition Custom Labels to train a new model.
F. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
Correct Answer: CEF
Reference: https://docs.aws.amazon.com/rekognition/latest/dg/how-it-works-types.html https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21 https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html
New Q25:
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company\’s AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that require GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?
A. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
B. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
C. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances. Store the resulting transcriptions in the transcription S3 bucket.
D. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
Correct Answer: A
New Q26:
A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in the Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.
Which set of actions should the ML specialist take to meet these requirements?
A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
C. Create an IAM role that has permission to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.
D. Create an IAM role that has permission to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
Correct Answer: A
New Q27:
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker
Correct Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html
New Q28:
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)
A. Emails exchanged by customers and the company\’s customer service agents
B. Social media posts containing the name of the company or its products
C. A publicly available collection of news articles
D. A publicly available collection of customer reviews
E. Product sales revenue figures for the company
F. Instruction manuals for the company\’s products
Correct Answer: ABD
An email exchange between customers and customer service would be a valuable data source.
…
Download the latest leads4pass MLS-C01 dumps with PDF and VCE: https://www.leads4pass.com/aws-certified-machine-learning-specialty.html (302 Q&A)
Read MLS-C01 exam questions(Q1-Q13): https://awsexamdumps.com/aws-certified-specialty-certification-new-mls-c01-dumps-with-real-qas/