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MLA-C01 — questions

Page 9 of 12 · 226 total questions.

MLA-C01 Question 162

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Topic 1 · Question 162

A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker AI. The company has not hosted a model on SageMaker AI 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?

  • ARegister the model in Amazon Elastic Container Registry (Amazon ECR). Use AWS Compute Optimizer for recommendations about instance types.
  • BRegister the model in the SageMaker Model Registry. Use SageMaker Autopilot for recommendations about instance types.
  • CRegister the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types. (correct answer)
  • DRegister the model in Amazon Elastic Container Registry (Amazon ECR). Use SageMaker Experiments for recommendations about instance types.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Register the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 163

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Topic 1 · Question 163

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?

  • AAdd more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
  • BImplement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation. (correct answer)
  • CUse principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers. Implement cross-entropy loss functions.
  • DAugment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.

MLA-C01 Question 164

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Topic 1 · Question 164

A company collects customer data every day. The company stores the data as compressed files in an Amazon S3 bucket that is partitioned by date. Every month, analysts download the data, process the data to check the data quality, and then upload the data to Amazon QuickSight dashboards. An ML engineer needs to implement a solution to automatically check the data quality before the data is sent to QuickSight. Which solution will meet these requirements with the LEAST operational overhead?

  • ARun an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality. (correct answer)
  • BUse an AWS Glue trigger to run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Create an AWS Glue job that loads the data into a PySpark DataFrame. Configure the job to apply custom functions and to evaluate the data quality.
  • CRun Python scripts on an AWS Lambda function every month to evaluate data quality. Configure the S3 bucket to invoke the Lambda function when objects are added to the S3 bucket.
  • DConfigure the S3 bucket to send event notifications to an Amazon Simple Queue Service (Amazon SQS) queue when objects are uploaded. Use Amazon CloudWatch insights every month for the SQS queue to evaluate the data quality.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality.

Explanation

AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data.

MLA-C01 Question 165

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Topic 1 · Question 165

A company has an ML model in Amazon SageMaker AI. 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?

  • AConfigure 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. (correct answer)
  • BConfigure 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.
  • CUse 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.
  • DUse Amazon CloudWatch to directly observe the SageMaker AI endpoint’s performance metrics. Manually analyze the CloudWatch logs for indicators of data drift or shifts in feature distribution.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option 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 c...

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 166

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Topic 1 · Question 166

A company is using Amazon SageMaker AI to deploy a new recommendation model for its ecommerce website. The model must use data from all client website interactions as input. Traffic is variable throughout the day. The company needs to create an inference endpoint for the model. Which type of inference endpoint will meet these requirements MOST cost-effectively?

  • ABatch transform inference endpoint
  • BAsynchronous inference endpoint
  • CReal-time inference endpoint
  • DServerless inference endpoint (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Serverless inference endpoint This option delivers the requirement at the lowest cost.

MLA-C01 Question 167

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Topic 1 · Question 167

A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain. Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address. Which update to the network configuration will meet this requirement?

  • ACreate a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  • BCreate a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located. (correct answer)
  • CCreate a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
  • DCreate a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.

MLA-C01 Question 168

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Topic 1 · Question 168

A company’s ML engineer is creating a classification model. The ML engineer explores the dataset and notices a column that is named day_of_week. The column’s data consists of the following values: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday. Which technique should the ML engineer use to convert this column’s data to binary values?

  • ABinary encoding
  • BLabel encoding
  • COne-hot encoding (correct answer)
  • DTokenization
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: One-hot encoding.

MLA-C01 Question 169

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Topic 1 · Question 169

An ML engineer wants to use Amazon SageMaker AI to prepare data for training. During exploratory data analysis, the ML engineer notices that several categorical features are missing values. How can the ML engineer use SageMaker AI to solve this problem?

  • AUse SageMaker Clarify to impute categorical features with the mean value.
  • BUse SageMaker Clarity to impute categorical features with the mode value.
  • CUse SageMaker Data Wrangler to impute categorical features with the mean value.
  • DUse SageMaker Data Wrangler to impute categorical features with the mode value. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use SageMaker Data Wrangler to impute categorical features with the mode value.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 170

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Topic 1 · Question 170

A company stores user clickstream data in an Amazon S3 bucket in AWS Account A. The company needs to use the data to train an ML model in Amazon SageMaker AI in AWS Account B. The training will take 10 days. The company needs to use only private IP addresses in the training. The company also must make sure that no training metadata is shared with AWS. Which solution will meet these requirements?

  • ASet up VPC peering between Account A and Account B. Contact AWS by email to opt out of metadata collection.
  • BSet up a VPC endpoint for the S3 bucket. Set the SageMaker AI OPT_OUT_TRACKING environment variable to 1 in the training job. (correct answer)
  • CConfigure a security group policy that is assigned to the S3 bucket in Account A to allow access from only Account B. Create AI services opt-out policies.
  • DGenerate presigned URLs with expiration times for the objects that are stored in the S3 bucket. Access the data by using the presigned URLs. Set the SageMaker AI OPT_OUT_TRACKING environment variable to 1 in the training job.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Set up a VPC endpoint for the S3 bucket. Set the SageMaker AI OPT_OUT_TRACKING environment variable to 1 in the training job.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. A VPC endpoint provides private connectivity to AWS services without traversing the public internet. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 171

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Topic 1 · Question 171

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?

  • AIngest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes. (correct answer)
  • BUse Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
  • CUse AWS Lake Formation. Apply tag-based control on the data.
  • DUse the Generate Data Insights function in SageMaker Data Wrangler.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 172

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Topic 1 · Question 172

A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance. Which solution will meet these requirements?

  • ASchedule a monitoring job in Amazon SageMaker Model Monitor. Generate the monitoring results for the model and data.
  • BCreate an Amazon CloudWatch dashboard that includes the metrics for processing steps in the nightly batch inference job. Compare the baseline resource metrics. Share the dashboard link.
  • CUse AWS Glue DataBrew to create a custom recipe job that uses the Numerical Statistics data quality check for the model file. Generate the results.
  • DCreate a SageMaker AI pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Create a SageMaker AI pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 173

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Topic 1 · Question 173

A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always be up to date with the latest changes in the source systems. Which solution will meet these requirements?

  • AUse direct connections to import data from the data sources into Data Wrangler. (correct answer)
  • BUse cataloged connections to import data from the data sources into Data Wrangler.
  • CUse AWS Glue to extract data from the data sources. Use AWS Glue also to import the data directly into Data Wrangler.
  • DUse AWS Lambda to extract data from the data sources. Use Lambda also to import the data directly into Data Wrangler.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use direct connections to import data from the data sources into Data Wrangler.

Explanation

AWS Direct Connect provides a dedicated, consistent private network link between on premises and AWS.

MLA-C01 Question 174

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Topic 1 · Question 174

An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data. Which metric should the ML engineer use to evaluate the model’s performance?

  • AAccuracy
  • BInferenceLatency
  • CArea Under the ROC Curve (AUC)
  • DRoot mean square error (RMSE) (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Root mean square error (RMSE).

MLA-C01 Question 175

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Topic 1 · Question 175

A company has built, trained, and tuned two new ML models: • Model A detects if a transaction is fraudulent based on the IP address, location, and user credentials. This model will be accessed every time a transaction occurs. • Model B forecasts sales totals for the next month based on historical sales data. This model will be accessed one time each month. The company must deploy both models to production by using Amazon SageMaker AI. Which hosting solution for the models should the company use to meet these requirements?

  • AHost both models in one container behind one real-time endpoint.
  • BHost Model A with an asynchronous endpoint. Host Model B with a real-time endpoint.
  • CHost Model A with a real-time endpoint. Use batch transform for Model B. (correct answer)
  • DUse batch transform for Model A. Host Model B with an asynchronous endpoint.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Host Model A with a real-time endpoint. Use batch transform for Model B.

Explanation

AWS Batch schedules and runs batch computing jobs at any scale without managing clusters.

MLA-C01 Question 176

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Topic 1 · Question 176

A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly supports. The model must be explainable to the bank’s regulators. Which modeling approach will meet these requirements?

  • ATrain the model by using the Object2Vec algorithm.
  • BTrain the model by using the linear learner algorithm. (correct answer)
  • CTrain a neural network.
  • DTrain the model by using the k-means algorithm.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Train the model by using the linear learner algorithm.

MLA-C01 Question 177

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Topic 1 · Question 177

A company is preparing data to train a new ML model on Amazon SageMaker AI. The data has not been used before for ML training. The data includes duplicates and is missing some values. The company needs to increase the data quality and detect any statistical bias in the data. Which solution will meet these requirements?

  • AUse SageMaker Clarify to create data quality rules. Use SageMaker Model Monitor to detect bias.
  • BUse SageMaker Data Wrangler to create data quality rules. Use SageMaker Clarify to detect bias. (correct answer)
  • CUse SageMaker Debugger to create data quality rules. Use SageMaker Model Monitor to detect bias.
  • DUse SageMaker Model Monitor to create data quality rules. Use SageMaker Clarify to detect bias.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use SageMaker Data Wrangler to create data quality rules. Use SageMaker Clarify to detect bias.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 178

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Topic 1 · Question 178

An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment. The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment. Which solution will meet these requirements?

  • ARemove the outliers and perform a log transformation on the Square Meters variable. (correct answer)
  • BKeep the outliers and perform normalization on the Square Meters variable.
  • CRemove the outliers and perform one-hot encoding on the Square Meters variable.
  • DKeep the outliers and perform one-hot encoding on the Square Meters variable.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Remove the outliers and perform a log transformation on the Square Meters variable.

MLA-C01 Question 179

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Topic 1 · Question 179

A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and must display the results on a dashboard. An ML engineer creates a bias monitoring job. How should the ML engineer capture bias metrics to display on the dashboard?

  • ACapture AWS CloudTrail metrics from SageMaker Clarify.
  • BCapture Amazon CloudWatch metrics from SageMaker Clarify. (correct answer)
  • CCapture SageMaker Model Monitor metrics from Amazon EventBridge.
  • DCapture SageMaker Model Monitor metrics from Amazon Simple Notification Service (Amazon SNS).
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Capture Amazon CloudWatch metrics from SageMaker Clarify.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 180

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Topic 1 · Question 180

A company is using an Amazon SageMaker AI ML model to predict traffic accidents that potholes cause. An ML engineer has configured SageMaker Model Monitor to run as part of a SageMaker AI pipeline. In the MonitoringExecution output, the ML engineer observes several baseline_drift_check violations that are failing the pipeline. What should the ML engineer do to resolve this issue?

  • ARetrain the model by using a new SageMaker AI training job. Check for errors by using SageMaker Debugger.
  • BRetrain the model with new training data. Reuse the original baseline in Model Monitor.
  • CRetrain the model with new training data. Use the new baseline in Model Monitor. (correct answer)
  • DRerun the SageMaker AI pipeline after enabling the emit_metrics option in the baseline constraints file.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Retrain the model with new training data. Use the new baseline in Model Monitor.

MLA-C01 Question 181

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Topic 1 · Question 181

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?

  • AF1 score
  • BArea Under the ROC Curve (AUC)
  • CPrecision
  • DRecall (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Recall.

Showing questions 161–180 of 226 · Page 9 of 12