AWS Machine Learning Certification

Know your Zeppelin from your Jupyter? How about Bring Your Own Algorithm vs Built-In? Give these questions a go and see whether you’re ready for certification.

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AWS Machine Learning – Quiz 4

1 / 8

QUESTION

The development team want to make use of SageMaker auto-scaling. Where should this be configured?

(Select One?)

2 / 8

QUESTION

A data & development team have built a deep learning model on a SageMaker TensorFlow Framework container. However, the model is performing poorly and training is frequently failing.

Which of the following approaches would help resolve the issue?

(Select One)

3 / 8

QUESTION

A developer is choosing between two different approaches for working with SageMaker – which of the following are true?

(Select Two)

 

4 / 8

QUESTION

A product owner has made the development team aware of a number of built-in  machine learning capabilities associated with various AWS Services. Match the machine learning capability with the AWS service.

a) Anomaly detection (RCF) + Hotspot detection
b) Duplicate detection (FindMatches ML)
c) Anomaly detection (RCF), Forecasting, Auto-Narratives
d) Active label learning

(Select One)

5 / 8

QUESTION

A data scientist is utilising SageMaker notebooks to explore various machine learning models with a subset of the data. Which of the following are true?

(Select Two)

6 / 8

QUESTION

A data scientist has been experimenting with a deep neural network, utilising Kaggle’s free Jupyter notebook environment, Keras and Tensorflow.

Which of the options corresponds to the minimal actions needed to get this running on SageMaker?

(Select One)

a. Load train & test data into S3
b. Modify your Kaggle Script to accept model directory, train and test, host arguments and to save the model
c. Add your Kaggle script (myscript.py) into your Notebook instance
d. Register Your Container on ECM
e.  Specify the Container location in the SageMaker estimator.
f. Create a SageMaker notebook and use myscript.py as the entry point in the TensorFlow estimator.
g. Use SageMaker Kaggle Sync to transfer the script file and model artifacts to S3.
h. Create a serve script

7 / 8

QUESTION

Which of the following must be set by the user (required hyperparameters), for SageMaker’s built-in algorithm, Linear Learner? (assume classification)

(Select Two)

8 / 8

QUESTION

A product owner is working with a data science team who use Spark on AWS EMR for  big data projects and have significant expertise with Spark. They have indicated a preference to work with Spark + MLLib to address the real-time predictions use case you’ve proposed.

Which of the following would you recommend as the most scalable and efficient way to proceed?

 

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