MLA-C01 practice questions 131–140

1. Question 131 · 226 in bank

   A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solution will meet these requirements?

   • ACreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
   • BCreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.
   • CCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew profile job.
   • DCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew recipe job.

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   Answer: B

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2. Question 132 · 226 in bank

   A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format. An ML engineer needs to convert the JSON files into a tabular format. Which solution will meet this requirement with the LEAST operational overhead?

   • ACreate an AWS Glue PySpark job that uses the Relationalize transform to convert the files.
   • BWrite custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
   • CCreate an AWS Lambda function that uses a Python runtime and invokes the reduce() function to convert the files. Invoke the Lambda function.
   • DCreate an Amazon Athena database that is based on the JSON files. Use the Athena flatten function to convert the data.

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   Answer: A

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3. Question 133 · 226 in bank

   A medical company ingests streams of data from devices that monitor patients’ vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features. An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration. Which solution will meet these requirements with the LEAST implementation effort?

   • AUse Amazon CloudWatch to create custom metrics for the characteristics of each iteration.
   • BWrite the characteristics of each iteration to logs in Amazon S3. Use AWS Glue and Amazon Athena to search the logs.
   • CUse the SageMaker Model Registry to track the characteristics and results of each iteration.
   • DUse SageMaker Experiments to track the characteristics and results of each iteration.

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   Answer: D

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4. Question 134 · 226 in bank

   Select 3

   A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of internal documents to address customer issues. The new solution must be serverless. Which combination of steps will meet these requirements?

   • ASet up Amazon Bedrock with the Anthropic Claude foundation model.
   • BSet up Amazon SageMaker JumpStart with the Llama foundation model.
   • CUse Amazon EC2 instances with Amazon API Gateway to invoke the model API.
   • DUse AWS Lambda functions with Amazon API Gateway to invoke the model API.
   • EUse an Amazon S3 bucket to store vector database dumps and embeddings.
   • FUse Amazon RDS for MySQL to store vector database dumps and embeddings.

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   Answer: ADE

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5. Question 135 · 226 in bank

   An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions. When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data. Which deployment solution will meet these requirements with the LEAST operational overhead?

   • ADeploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
   • BDeploy the model to an Amazon SageMaker real-time endpoint.
   • CDeploy the model to an Amazon SageMaker Asynchronous Inference endpoint.
   • DPackage the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.

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   Answer: C

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6. Question 136 · 226 in bank

   Select 2

   An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either “yes” or “no.” The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset. Which methods will meet these requirements? (Choose two.)

   • ABinary encoding
   • BLabel encoding
   • COne-hot encoding
   • DStatistical imputation
   • ETokenization

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   Answer: AB

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7. Question 137 · 226 in bank

   A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker algorithm should the company choose for the recommendation model?

   • AK-nearest neighbors (k-NN)
   • BFactorization Machines
   • CPrincipal component analysis (PCA)
   • DSequence-to-Sequence (seq2seq)

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   Answer: B

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8. Question 138 · 226 in bank

   A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks. The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry. Which solution will meet these requirements with the LEAST operational overhead?

   • AExport the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
   • BImport the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
   • CUse code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
   • DImport the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.

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   Answer: D

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9. Question 139 · 226 in bank

   A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credit card data. An ML engineer must implement a solution to train and deploy the LLM on Amazon SageMaker. Which solution will meet these requirements?

   • AUse SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real-time inference.
   • BUse SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference.
   • CUse SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker Asynchronous Inference.
   • DUse SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.

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   Answer: A

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10. Question 140 · 226 in bank

    A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1. The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2. Which solution will meet these requirements?

    • ACreate a second production variant for v2. Assign 1% of the traffic to v2 and 99% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
    • BCreate a second production variant for v2. Assign 10% of the traffic to v2 and 90% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
    • CDeploy v2 to a new endpoint. Turn on data capturing for the production endpoint. Write a script to pass 100% of input data to v2. If v2 performs as expected, deactivate the v1 endpoint and direct the traffic to v2.
    • DDeploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production.

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    Answer: D

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MLA-C01 practice, question 131. 226 questions available.