Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 241:

  An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10x10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.

  The company wants to build a weed detection mode! that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.

  Which approach should a Machine Learning Specialist take to obtain accurate predictions?

  A. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

  B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

  C. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

  D. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

  Correct Answer: C

  To detect specific types of weeds requires object detection. Object detection algorithms are designed to identify objects and their locations within an image.

  Single-shot multibox detectors (SSD) are a type of object detection algorithm that are well-suited for real-time inferencing and have been shown to be effective for a variety of object detection tasks.

  By preparing the images in RecordIO format and using Amazon SageMaker, the company can easily train, test, and validate the model, making it easier to deploy the model in a scalable and secure environment.

• Question 242:

  A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.

  Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

  A. Set PerformAutoML to true.

  B. Set ForecastHorizon to 4.

  C. Set ForecastFrequency to W for weekly.

  D. Set PerformHPO to true.

  E. Set FeaturizationMethodName to filling.

  Correct Answer: AD

  Setting PerformAutoML to true will enable Amazon Forecast to automatically select the best algorithm and hyperparameters for your data and problem. This can help improve the MAPE by finding the optimal combination of algorithm and hyperparameters that minimize prediction error.

  Setting PerformHPO to true will enable Amazon Forecast to perform a hyperparameter optimization search to find the best combination of hyperparameters that result in the best prediction performance. This can help improve the MAPE by finding the optimal combination of hyperparameters that minimize prediction error.

• Question 243:

  An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.

  How should a machine learning specialist meet these requirements?

  A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.

  B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.

  C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.

  D. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

  Correct Answer: A

  This solution meets the requirements of data localization regulations and secure data transfer. By creating an AWS Glue job to connect to the PostgreSQL DB instance, the machine learning specialist can extract tables without sensitive data. By using a Site-to-Site VPN connection, the data can be securely transferred from the on-premises data center to Amazon S3, where it can be used for model retraining. This solution ensures that any sensitive data remains in the on-premises data center, and that only non-sensitive data is uploaded to the cloud.

  https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect.html#aws-glue-programming-etl-connect-jdbc https://aws.amazon.com/blogs/big-data/how-to-access-and-analyze-on-premises-data-stores-using-aws-glue/

• Question 244:

  A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.

  Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

  A. Add a VPC endpoint policy to allow access to the IAM users.

  B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.

  C. Modify the security group on the endpoint network interface to restrict access to the instances.

  D. Modify the ACL on the endpoint network interface to restrict access to the instances.

  E. Add a SageMaker Runtime VPC endpoint interface to the VPC.

  Correct Answer: AC

  Reference: https://aws.amazon.com/blogs/machine-learning/securing-all-amazon-sagemaker-api-calls-with-aws-privatelink/

• Question 245:

  The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

  Which machine learning algorithm should the researchers use that BEST meets their requirements?

  A. Latent Dirichlet Allocation (LDA)

  B. Recurrent neural network (RNN)

  C. K-means

  D. Convolutional neural network (CNN)

  Correct Answer: D

• Question 246:

  A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

  How should a data scientist adjust the solution?

  A. Use the event tracker in Amazon Personalize to include real-time user interactions.

  B. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

  C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

  D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

  Correct Answer: A

  https://docs.aws.amazon.com/personalize/latest/dg/maintaining-relevance.html

• Question 247:

  A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.

  Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

  A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.

  B. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

  C. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.

  D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

  Correct Answer: B

  Amazon SageMaker script mode enables training a machine learning model using a script that you provide. By using the unchanged train.py script and putting the TFRecord data into an Amazon S3 bucket, you can easily point the Amazon

  SageMaker training invocation to the S3 bucket without reformatting the training data.

  Option B avoids rewriting the train.py script or preparing the data in a different format. It also leverages the scalability and cost-effectiveness of Amazon S3 for storing large amounts of data, which is important for training machine learning

  models.

  https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html

  https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py

• Question 248:

  A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.

  To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.

  Which deployment architecture for the model will address these business requirements?

  A. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.

  B. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.

  C. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

  D. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.

  Correct Answer: B

  https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/ https://aws.amazon.com/blogs/iot/industrial-iot-from-condition-based-monitoring-to-predictive-quality-to-digitize-your-factory-with-aws-iot-services/

• Question 249:

  A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

  

  What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

  A. Exponential transformation

  B. Logarithmic transformation

  C. Polynomial transformation

  D. Sinusoidal transformation

  Correct Answer: B

  As this feature seems skewed while others have a regular distribution according to the question. The log transformation will reduce this features skewness.

• Question 250:

  A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

  

  Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  A. Increase the max_depth parameter value.

  B. Lower the max_depth parameter value.

  C. Update the objective to binary:logistic.

  D. Lower the min_child_weight parameter value.

  Correct Answer: B