AWS-Certified-Machine-Learning-Specialty Free Practice Test

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

1. A. Detecting seasonality for the majority of stores will be an issu
2. B. Request categorical data to relate new stores with similar stores that have more historical data.
3. C. The sales data does not have enough varianc
4. D. Request external sales data from other industries to improve the model's ability to generalize.
5. E. Sales data is aggregated by wee
6. F. Request daily sales data from the source database to enable building a daily model.
7. G. The sales data is missing zero entries for item sale
8. H. Request that item sales data from the source database include zero entries to enable building the model.
9. I. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Correct Answer: AB

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

1. A. Specificity
2. B. False positive rate
3. C. Accuracy
4. D. Area under the precision-recall curve
5. E. True positive rate

Correct Answer: DE

An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

1. A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
2. B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
3. C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
4. D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Correct Answer: C

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?

1. A. Call the CreateNotebookInstanceLifecycleConfig API operation
2. B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
3. C. Stop and then restart the SageMaker notebook instance
4. D. Call the UpdateNotebookInstanceLifecycleConfig API operation

Correct Answer: C

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?

1. A. A naive Bayesian model, since the features are all conditionally independent.
2. B. A full Bayesian network, since the features are all conditionally independent.
3. C. A naive Bayesian model, since some of the features are statistically dependent.
4. D. A full Bayesian network, since some of the features are statistically dependent.

Correct Answer: C