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NEW QUESTION 39
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? (Choose two.)

  • A. 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.
  • B. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
  • C. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
  • D. Customize the built-in image classification algorithm to use Inception and use this for model training.
  • E. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

Answer: B,E

 

NEW QUESTION 40
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

  • A. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
  • B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
  • C. Train a custom ARIMA model to forecast demand for the new product.
  • D. Train a custom XGBoost model to forecast demand for the new product

Answer: A

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

 

NEW QUESTION 41
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
MLS-C01-cfb4202a3eba1dbae66b15da6cea2df1.jpg
Which technique can be used to improve model performance in terms of accuracy in the validation set?

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

Answer: D

 

NEW QUESTION 42
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non- fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.
MLS-C01-b038f28800fd6885b207cf6c9400aa45.jpg
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)

  • A. Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.
  • B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • C. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: A,B

 

NEW QUESTION 43
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