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NEW QUESTION 38
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

A. Linear regression
B. Clustering
C. Classification
D. Reinforcement learning

Answer: C

Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?

NEW QUESTION 39
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of
4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

A. Drop all records from the dataset where age has been set to 0.
B. Drop the age feature from the dataset and train the model using the rest of the features.
C. Use k-means clustering to handle missing features
D. Replace the age field value for records with a value of 0 with the mean or median value from the dataset

Answer: D

Explanation:
For k-means you should do additional derivation of feasible number of clusters which is not a trivial task.

NEW QUESTION 40
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

A. Linear regression
B. Clustering
C. Classification
D. Reinforcement learning

Answer: C

Explanation:
Explanation
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?

NEW QUESTION 41
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
B. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
C. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
D. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

Answer: B

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

NEW QUESTION 42
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.
What is the MOST likely cause of this issue?

A. The class distribution in the dataset is imbalanced.
B. The batch size is too big.
C. The learning rate is very high.
D. Dataset shuffling is disabled.

Answer: C

Explanation:
https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95

NEW QUESTION 43
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