Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 211:

  A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.

  What should the ML specialist do to improve the model results?

  A. Increase the L1 regularization parameter. Do not change any other training parameters.
  B. Decrease the L1 regularization parameter. Do not change any other training parameters.
  C. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
  D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.
  B. Decrease the L1 regularization parameter. Do not change any other training parameters.

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  Explanation

• Question 212:

  An energy company has wind turbines, weather stations, and solar panels that generate telemetry data. The company wants to perform predictive maintenance on these devices. The devices are in various locations and have unstable internet connectivity.

  A team of data scientists is using the telemetry data to perform machine learning (ML) to conduct anomaly detection and predict maintenance before the devices start to deteriorate. The team needs a scalable, secure, high-velocity data ingestion mechanism. The team has decided to use Amazon S3 as the data storage location.

  Which approach meets these requirements?

  A. Ingest the data by using an HTTP API call to a web server that is hosted on Amazon EC2. Set up EC2 instances in an Auto Scaling configuration behind an Elastic Load Balancer to load the data into Amazon S3.
  B. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to use Amazon Kinesis Data Firehose to send data to an Amazon Kinesis data stream that is configured to write to an S3 bucket.
  C. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to direct all MQTT data to an Amazon Kinesis Data Firehose delivery stream that is configured to write to an S3 bucket.
  D. Ingest the data over Message Queuing Telemetry Transport (MQTT) to Amazon Kinesis data stream that is configured to write to an S3 bucket.
  C. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to direct all MQTT data to an Amazon Kinesis Data Firehose delivery stream that is configured to write to an S3 bucket.

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  Explanation

• Question 213:

  A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model

  What should the Specialist do to prepare the data for model training'?

  A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution
  B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude
  C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude
  D. Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
  C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude

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  Explanation

  https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

• Question 214:

  While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown. What does this mean?

  

  A. The model might have prediction errors over a range of target values.
  B. The dataset cannot be accurately represented using the regression model
  C. There are too many variables in the model
  D. The model is predicting its target values perfectly.
  D. The model is predicting its target values perfectly.

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  Explanation

• Question 215:

  A data scientist for a medical diagnostic testing company has developed a machine learning (ML) model to identify patients who have a specific disease. The dataset that the scientist used to train the model is imbalanced. The dataset contains a large number of healthy patients and only a small number of patients who have the disease. The model should consider that patients who are incorrectly identified as positive for the disease will increase costs for the company.

  Which metric will MOST accurately evaluate the performance of this model?

  A. Recall
  B. F1 score
  C. Accuracy
  D. Precision
  D. Precision

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  Explanation

• Question 216:

  A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.

  What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

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  Explanation

  https://sagemaker-workshop.com/custom/containers.html

• Question 217:

  A data engineer is evaluating customer data in Amazon SageMaker Data Wrangler. The data engineer will use the customer data to create a new model to predict customer behavior.

  The engineer needs to increase the model performance by checking for multicollinearity in the dataset.

  Which steps can the data engineer take to accomplish this with the LEAST operational effort? (Choose two.)

  A. Use SageMaker Data Wrangler to refit and transform the dataset by applying one-hot encoding to category-based variables.
  B. Use SageMaker Data Wrangler diagnostic visualization. Use principal components analysis (PCA) and singular value decomposition (SVD) to calculate singular values.
  C. Use the SageMaker Data Wrangler Quick Model visualization to quickly evaluate the dataset and to produce importance scores for each feature.
  D. Use the SageMaker Data Wrangler Min Max Scaler transform to normalize the data.
  E. Use SageMaker Data Wrangler diagnostic visualization. Use least absolute shrinkage and selection operator (LASSO) to plot coefficient values from a LASSO model that is trained on the dataset.
  A. Use SageMaker Data Wrangler to refit and transform the dataset by applying one-hot encoding to category-based variables.
  E. Use SageMaker Data Wrangler diagnostic visualization. Use least absolute shrinkage and selection operator (LASSO) to plot coefficient values from a LASSO model that is trained on the dataset.

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  Explanation

• Question 218:

  A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.

  An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.

  Which completion criteria will meet this requirement?

  A. MaxRuntimeInSeconds
  B. TargetObjectiveMetricValue
  C. CompleteOnConvergence
  D. MaxNumberOfTrainingJobsNotImproving
  A. MaxRuntimeInSeconds

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  Explanation

• Question 219:

  A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.

  The data scientist wants to understand the variance in the data along various directions in the feature space.

  Which solution will meet these requirements?

  A. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.
  B. Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.
  C. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.
  D. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.
  C. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.

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  Explanation

  Principal Component Analysis (PCA) is a dimensionality reduction technique that captures the variance within the feature space, helping to understand the directions in which data varies most. In SageMaker Data Wrangler, the multicollinearity measurement and PCA features allow the data scientist to analyze interdependencies between predictor variables while reducing redundancy. PCA transforms correlated features into a set of uncorrelated components, helping to simplify the dataset without significant loss of information, making it ideal for refining features based on variance. Options A and D offer methods to understand feature relevance but are less effective for managing multicollinearity and variance representation in the data.

• Question 220:

  A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.

  A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations. Which AWS service or feature will meet these requirements with the LEAST development effort over time?

  A. AWS Glue jobs
  B. Amazon EMR cluster
  C. Amazon Athena
  D. AWS Lambda
  A. AWS Glue jobs

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  Explanation

  AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating data. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1. The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.

  References:

  1: AWS Glue - Fully Managed ETL Service - Amazon Web Services

  2: Amazon EMR - Amazon Web Services

  3: Amazon Athena -Interactive SQL Queries for Data in Amazon S3 [4]: AWS Lambda -Serverless Compute - Amazon Web Services