Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 261:

  A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.

  Which action will resolve the problem?

  A. Change preprocessing to use n-grams.
  B. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
  C. Adjust hyperparameters related to the attention mechanism.
  D. Choose a different weight initialization type.
  C. Adjust hyperparameters related to the attention mechanism.

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  Explanation

• Question 262:

  A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.

  Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

  A. Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
  B. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.
  C. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
  D. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.
  C. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

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  Explanation

• Question 263:

  A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.

  The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes

  What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  A. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  B. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
  C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
  B. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

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  Explanation

• Question 264:

  Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an IAM policy that allows data scientists to access both tables.

  A. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  B. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
  C. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  D. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
  C. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

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  Explanation

• Question 265:

  A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.

  What approach should the Specialist take to accomplish these tasks?

  A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
  B. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
  C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
  D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
  A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.

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  Explanation

  https://towardsdatascience.com/an-introduction-to-t-sne-with-python-example-5a3a293108d1

• Question 266:

  A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.

  The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint. However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures. The data scientist's IAM user is currently unable to invoke the SageMaker endpoint.

  Which combination of actions will give the data scientist's IAM user the ability to invoke the SageMaker endpoint? (Select THREE.)

  A. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
  B. Include a policy statement for the data scientist's IAM user that allows the IAM user to perform the sagemaker:InvokeEndpoint action.
  C. Include an inline policy for the data scientist's IAM user that allows SageMaker to read S3 objects.
  D. Include a policy statement for the data scientist's IAM user that allows the IAM user to perform the sagemaker:GetRecord action.
  E. Include the SQL statement USING EXTERNAL FUNCTION ml_function_name in the Athena SQL query.
  F. Perform a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint.
  B. Include a policy statement for the data scientist's IAM user that allows the IAM user to perform the sagemaker:InvokeEndpoint action.
  C. Include an inline policy for the data scientist's IAM user that allows SageMaker to read S3 objects.
  E. Include the SQL statement USING EXTERNAL FUNCTION ml_function_name in the Athena SQL query.

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  Explanation

  The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:

  B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted

  at the endpoint.

  C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries.

  E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML

  models from SQL statements.

  The other options are not correct or necessary, because they have the following drawbacks:

  A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model.

  D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this

  scenario.

  F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.

  References:

  1: InvokeEndpoint - Amazon SageMaker

  2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena

  3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog

  4: AmazonAthenaFullAccess - AWS Identity and Access Management

  5: GetRecord - Amazon SageMaker Feature Store Runtime : [Invoke a Multi-Model Endpoint - Amazon SageMaker]

• Question 267:

  A company's data engineer wants to use Amazon S3 to share datasets with data scientists. The data scientists work in three departments: Finance. Marketing, and Human Resources. Each department has its own IAM user group. Some datasets contain sensitive information and should be accessed only by the data scientists from the Finance department.

  How can the data engineer set up access to meet these requirements?

  A. Create an S3 bucket for each dataset. Create an ACL for each S3 bucket. For each S3 bucket that contains a sensitive dataset, set the ACL to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.
  B. Create an S3 bucket for each dataset. For each S3 bucket that contains a sensitive dataset, set the bucket policy to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.
  C. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. For the Finance department user group, attach an IAM policy that provides access to both folders. For the Marketing and Human Resources department user groups, attach an IAM policy that provides access to only the folder that contains the non-sensitive datasets.
  D. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. Set the policy for the S3 bucket to allow only the Finance department user group to access the folder that contains the sensitive datasets. Allow all three department user groups to access the folder that contains the non-sensitive datasets.
  C. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. For the Finance department user group, attach an IAM policy that provides access to both folders. For the Marketing and Human Resources department user groups, attach an IAM policy that provides access to only the folder that contains the non-sensitive datasets.

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  Explanation

  https://stackoverflow.com/questions/35944349/iam-aws-s3-to-restrict-to-a-specific-sub-folder https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/

• Question 268:

  A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3. Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

  A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
  B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
  C. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
  D. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
  E. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
  A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
  C. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

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  Explanation

  https://towardsdatascience.com/sales-forecasting-from-time-series-to-deep-learning-5d115514bfac https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf

• Question 269:

  An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.

  Which approach should the ML specialist use to improve the performance of the model on the testing data?

  A. Increase the value of the momentum hyperparameter.
  B. Reduce the value of the dropout_rate hyperparameter.
  C. Reduce the value of the learning_rate hyperparameter
  D. Increase the value of the L2 hyperparameter.
  D. Increase the value of the L2 hyperparameter.

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  Explanation

  Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used. https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

• Question 270:

  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)
  D. Area Under the ROC Curve (AUC)

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  Explanation

  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.