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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q156-Q161):

NEW QUESTION # 156
A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

• A. Compressed JSON
• B. Parquet files
• C. RecordlO
• D. CSV files

Answer: B

NEW QUESTION # 157
A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?

• A. Use AWS Glue to compress the data into the Apache Parquet format.
• B. Use the SageMaker hyperparameter optimization feature to automatically optimize the data.
• C. Transform the dataset into the RecordIO protobufformat.
• D. Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Answer: C

NEW QUESTION # 158
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

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

• A. Adding another layer with the 100 neurons
• B. Increasing the number of epochs
• C. Random initialization of weights with appropriate seed
• D. Early stopping

Answer: A

NEW QUESTION # 159
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

• A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
• B. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
• C. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
• D. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Answer: C

NEW QUESTION # 160
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

• A. Univariate selection
• B. Correlation plot with heat maps
• C. Data scaling with standardization and normalization
• D. Feature importance with a tree-based classifier
• E. Data binning
• F. Data augmentation

Answer: A,E,F

Explanation:
Reference:
https://towardsdatascience.com/feature-selection-using-python-for-classification-problem-b5f00a1c7028#:~:text=Univariate%20feature%20selection%20works%20by,analysis%20of%20variance%20(ANOVA).&text=That%20is%20why%20it%20is%20called%20'univariate'
https://arxiv.org/abs/2101.04530

NEW QUESTION # 161
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