Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 111:

  HOTSPOT

  A company needs to train an ML model using historical transaction data to predict customer behavior. Choose the appropriate AWS service for each task related to handling this data. Each service should be selected only once or not at all.

  Tasks and corresponding AWS services:

  1. Store historical transaction data: Amazon S3

  2. Extract, transform, and load (ETL) data for training: AWS Glue

  3. Run SQL queries on the data for analysis: Amazon Athena

  

  

• Question 112:

  A company utilizes Amazon SageMaker to run machine learning models on accelerated instances, requiring real-time responses. Each model has distinct scaling requirements, and cold starts must be avoided. Which solution will effectively satisfy these requirements?

  A. Create a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency for the endpoints.
  B. Create a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling policy for each endpoint.
  C. Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1.
  D. Create an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker multi-model endpoint. Point the endpoint to the S3 bucket. Create an auto scaling policy for the endpoint. Set the parameter for the minimum number of copies to at least 1.
  C. Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1.

• Question 113:

  A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model?

  A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
  B. Implement padding to ensure that all color feature vectors have the same length.
  C. Perform dimensionality reduction on the color categories.
  D. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
  D. One-hot encode the color categories to transform the color scheme feature into a binary matrix.

• Question 114:

  A company uses a compute-optimized instance on Amazon SageMaker to run training jobs, with a consistent demand of 35 hours per week for the next 55 weeks. The company aims to minimize its model training costs. Which solution would effectively reduce costs while meeting these requirements?

  A. Use a serverless endpoint with a provisioned concurrency of 35 hours for each week. Run the training on the endpoint.
  B. Use SageMaker Edge Manager for the training. Specify the instance requirement in the edge device configuration. Run the training.
  C. Use the heterogeneous cluster feature of SageMaker Training. Configure the instance_type, instance_count, and instance_groups arguments to run training jobs.
  D. Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.
  D. Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.

• Question 115:

  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

  monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company is experimenting with consecutive training jobs.

  How can the company MINIMIZE infrastructure startup times for these jobs?

  A. Use Managed Spot Training.
  B. Use SageMaker managed warm pools.
  C. Use SageMaker Training Compiler.
  D. Use the SageMaker distributed data parallelism (SMDDP) library.
  B. Use SageMaker managed warm pools.

• Question 116:

  A company receives daily .csv files containing information about customer interactions with its ML model. These files are stored in Amazon S3 and are used to retrain the model. An ML engineer must implement a solution to mask credit card numbers in the files before they are used for retraining. The solution must require the least amount of development effort.

  Which approach would best fulfill these requirements?

  A. Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
  B. Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.
  C. Create Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
  D. Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.
  B. Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.

• Question 117:

  A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

  A. Hyperband
  B. Grid search
  C. Bayesian optimization
  D. Random search
  A. Hyperband

• Question 118:

  You need to preprocess data by normalizing and scaling numerical features in Amazon SageMaker. Which tool should you use?

  A. Feature Store
  B. Data Wrangler
  C. SageMaker Neo
  D. SageMaker Ground Truth
  B. Data Wrangler

• Question 119:

  A company has developed an ML model that leverages historical transaction data to predict customer behavior. An ML engineer is working on optimizing the model in Amazon SageMaker to improve its predictive accuracy. To do so, the engineer needs to analyze the input data and the corresponding predictions to uncover trends that might bias the model's performance across various demographic groups.

  Which solution would enable this level of detailed analysis?

  A. Use Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
  B. Create AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
  C. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.
  D. Create AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.
  C. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.

• Question 120:

  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?

  A. 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.
  B. 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.
  C. 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.
  D. 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.
  A. 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.