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NEW QUESTION 50
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Poisson distribution
• B. Binomial distribution
• C. Uniform distribution
• D. Normal distribution

Answer: B

NEW QUESTION 51
Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?

• A. F1 Score
• B. RMSE
• C. F Score with higher precision weighting than recall
• D. F Score with higher recall weighted than precision

Answer: D

NEW QUESTION 52
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

• A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
• B. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
• C. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
• D. One feature obtained as an element-wise product between latitude, longitude, and car type

Answer: B

Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding

NEW QUESTION 53
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

• A. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
• B. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
• C. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
• D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

Answer: C

NEW QUESTION 54
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

• A. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
• B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
• C. Use the "Other Products You May Like" recommendation type to increase the click-through rate
• D. Import your user events and then your product catalog to make sure you have the highest quality event stream

Answer: D

NEW QUESTION 55
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