Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 281:

  A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.

  Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.)

  A. Add L1 regularization to the classifier

  B. Add features to the dataset

  C. Perform recursive feature elimination

  D. Perform t-distributed stochastic neighbor embedding (t-SNE)

  E. Perform linear discriminant analysis

  Correct Answer: AC

  Key Words:

  1.

  100 continuous numerical features ?too many features

  2.

  No feature selection has been done

  3.

  Easy interpretation - direct relationship between X and Y are preferred

  4.

  gap between the training and validation set accuracy ?overfitting

  A: L1 regularization solves overfitting, interpretation is easy, direct relationships between x and y

  B: More features, Overfitting will be worse.

  C: Recursive feature elimination solves overfitting, interpretation is easy, direct relationships between x and y

  D: Perform t-distributed stochastic neighbor embedding (t-SNE)= Amazon's favorite dimensionality reduction technique, frequently show up in the questions. However, same as PCA, less interpretable. You won't be able to see the direct impact of relevant features on the model outcome.

  E: If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique.

• Question 282:

  A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours

  With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?

  A. A histogram showing whether the most important input feature is Gaussian.

  B. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.

  C. A scatter plot showing (he performance of the objective metric over each training iteration

  D. A scatter plot showing the correlation between maximum tree depth and the objective metric.

  Correct Answer: D

• Question 283:

  For the given confusion matrix, what is the recall and precision of the model?

  

  A. Recall = 0.92 Precision = 0.84

  B. Recall = 0.84 Precision = 0.8

  C. Recall = 0.92 Precision = 0.8

  D. Recall = 0.8 Precision = 0.92

  Correct Answer: C

• Question 284:

  A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.

  How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

  A. Create a NAT gateway within the corporate VPC.

  B. Route Amazon SageMaker traffic through an on-premises network.

  C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

  D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.

  Correct Answer: C

  https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (516) https://docs.aws.amazon.com/zh_tw/vpc/latest/userguide/vpc-endpoints.html

• Question 285:

  A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.

  How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

  A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.

  B. onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.

  C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.

  D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

  Correct Answer: C

  "You don't need to specify the AWS KMS key ID when you download an SSE-KMS-encrypted object from an S3 bucket. Instead, you need the permission to decrypt the AWS KMS key.

  When a user sends a GET request, Amazon S3 checks if the AWS Identity and Access Management (IAM) user or role that sent the request is authorized to decrypt the key associated with the object. If the IAM user or role belongs to the same AWS account as the key, then the permission to decrypt must be granted on the AWS KMS key's policy." https://aws.amazon.com/premiumsupport/knowledge-center/decrypt-kms-encrypted-objects-s3/?nc1=h_ls

• Question 286:

  A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?

  A. Write a direct connection to the SQL database within the notebook and pull data in

  B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

  C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in

  D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

  Correct Answer: B

  https://www.slideshare.net/AmazonWebServices/train-models-on-amazon-sagemaker-using-data-not-from-amazon-s3-aim419-aws-reinvent-2018

• Question 287:

  A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

  What actions would allow the Specialist to get relevant numerical representations?

  A. Reduce image resolution and use reduced resolution pixel values as features

  B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

  C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

  D. Average colors by channel to obtain three-dimensional representations of images.

  Correct Answer: A

• Question 288:

  A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.

  Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

  A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

  B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

  C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

  D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

  Correct Answer: B

  Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

• Question 289:

  A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

  

  Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

  A. Decision tree

  B. Linear support vector machine (SVM)

  C. Naive Bayesian classifier

  D. Single Perceptron with sigmoidal activation function

  Correct Answer: C

  https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py

• Question 290:

  A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

  Which approach allows the Specialist to use all the data to train the model?

  A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

  B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

  C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

  D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

  Correct Answer: A