Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 291:

  A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.

  What does the Specialist need to do?

  A. Bundle the NVIDIA drivers with the Docker image.

  B. Build the Docker container to be NVIDIA-Docker compatible.

  C. Organize the Docker container's file structure to execute on GPU instances.

  D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

  Correct Answer: B

  https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf page 55: If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible. Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image. For more information about nvidia-docker, see NVIDIA/nvidia-docker.

• Question 292:

  A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available

  Which model is MOST likely to provide the best results in Amazon SageMaker?

  A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

  B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

  C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

  D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

  Correct Answer: C

  Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI

• Question 293:

  A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

  What should the Specialist do to meet this objective?

  A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

  B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

  C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

  D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

  Correct Answer: B

  Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

  Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/

• Question 294:

  A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

  What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

  A. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

  B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

  C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

  D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

  Correct Answer: B

  https://docs.aws.amazon.com/sagemaker/latest/dg/logging-using-cloudtrail.html

  AWS CloudTrail provides a history of AWS API calls made on the account. The Machine Learning team can use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. They can then use CloudWatch to create alarms and receive notifications when the model is overfitting.

  To ensure auditors can view the Amazon SageMaker log activity report, the team can add code to push a custom metric to Amazon CloudWatch. This provides a single place to view and analyze logs across all the services and resources in the environment

• Question 295:

  This graph shows the training and validation loss against the epochs for a neural network

  The network being trained is as follows

  1. Two dense layers one output neuron

  2.

  100 neurons in each layer

  4. Random initialization of weights

  

  3.

  100 epochs

  Which technique can be used to improve model performance in terms of accuracy in the validation set?

  A. Early stopping

  B. Random initialization of weights with appropriate seed

  C. Increasing the number of epochs

  D. Adding another layer with the 100 neurons

  Correct Answer: A

  The answer is Early Stopping. Stopp the training before accuracy start do decrease.

• Question 296:

  A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

  A. Use an Amazon SageMaker notebook for both feature engineering and model development

  B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

  C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

  D. Use Amazon ML for both feature engineering and model development.

  Correct Answer: B

• Question 297:

  A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.

  Which is the MOST suitable predictive model that can be deployed into production?

  A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.

  B. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data

  C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.

  D. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

  Correct Answer: A

  A recurrent neural network (RNN) is a more suitable choice than a convolutional neural network (CNN) because the data collected from the engines is a sequence of values over time, and the goal is to predict a future event (an engine fault). RNNs are designed to handle sequential data and can learn patterns and dependencies over time, making them well-suited for time-series data like this.

  On the other hand, CNNs are designed for image processing and are not ideal for sequential data.

• Question 298:

  A Data Engineer needs to build a model using a dataset containing customer credit card information

  How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

  A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

  B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

  C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

  D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

  Correct Answer: D

• Question 299:

  A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis

  What is the MOST efficient way to accomplish these tasks'?

  A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3

  B. Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake

  C. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

  D. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

  Correct Answer: A

  https://aws.amazon.com/tw/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random-cut-forest-algorithm-for-anomaly-detection/

• Question 300:

  A large consumer goods manufacturer has the following products on sale:

  1.

  34 different toothpaste variants

  2.

  48 different toothbrush variants

  3.

  43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.

  Which solution should a Machine Learning Specialist apply?

  A. Train a custom ARIMA model to forecast demand for the new product.

  B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

  C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

  D. Train a custom XGBoost model to forecast demand for the new product

  Correct Answer: B

  The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

  Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html