Amazon MLS-C01 Quiz 1 Topic 6 Questions 1-5

Question: 1

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers

want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored

for offline analysis.

What approach would be the MOST effective to perform near-real time defect detection?

AUse AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within
AWS IoT Analytics to carry out analysis for anomalies.

BUse Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out
Apache Spark ML k-means clustering to determine anomalies.

CUse Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut
Forest (RCF) algorithm to determine anomalies.

DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest
(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further
analysis.

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Question: 2

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network

for a classification problem. The Specialist computes the Pearson correlation coefficients between each

feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

AA naive Bayesian model, since the features are all conditionally independent.

BA full Bayesian network, since the features are all conditionally independent.

CA naive Bayesian model, since some of the features are statistically dependent.

DA full Bayesian network, since some of the features are statistically dependent.

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Question: 3

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

q3_MLS-C01

Considering the graph, what is a reasonable selection for the optimal choice of k?

A1

B4

C7

D10

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Question: 4

A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.

Which solution can the team build MOST quickly to meet these requirements?

AUse Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.

BUse an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

CUse Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.

DUse Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

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Question: 5

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora

* Profiles for all past and existing customers

* Profiles for all past and existing insured pets

* Policy-level information

* Premiums received

* Claims paid

What steps should be taken to implement a machine learning model to identify potential new customers on social media?

AUse regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

BUse clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

CUse a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

DUse a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

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Amazon MLS-C01 Quiz:1 Topic:6 Questions:1-5