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  • Exam Name: AWS Certified Machine Learning - Specialty
  • Last Update: 02-Dec-2022
  • Questions and Answers: 208
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MLS-C01 Questions and Answers

Question # 1

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes

Which function will produce the desired output?

A.

Dropout

B.

Smooth L1 loss

C.

Softmax

D.

Rectified linear units (ReLU)

Question # 2

A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

Which approach should an ML specialist take to implement the new security feature in the web application?

A.

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

B.

Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.

C.

Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

D.

Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

Question # 3

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to

implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used

downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use,

if needed.

The solution needs to do the following:

  • Calculate an anomaly score for each web traffic entry.
  • Adapt unusual event identification to changing web patterns over time.

Which approach should the data scientist implement to meet these requirements?

A.

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random

Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic

data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model

to calculate the anomaly score for each record.

B.

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in

XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a

preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to

calculate the anomaly score for each record.

C.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input

source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data

with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a

tumbling window.

D.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input

source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data

with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record

using a sliding window.

Question # 4

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily

Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A.

Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

B.

Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

C.

Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

D.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

Question # 5

A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.

Which of the following will accomplish this? (Select TWO.)

A.

Customize the built-in image classification algorithm to use Inception and use this for model training.

B.

Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

C.

Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

D.

Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.

E.

Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

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