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NEW QUESTION 37
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

• A. Predict the missing values using linear regression.
• B. Delete the rows that have missing values.
• C. Apply feature crossing with another column that does not have missing values.
• D. Replace the missing values with zeros.

Answer: A

NEW QUESTION 38
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. F Score with higher recall weighted than precision
• C. F Score with higher precision weighting than recall
• D. RMSE

Answer: B

NEW QUESTION 39
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. Use the "Other Products You May Like" recommendation type to increase the click-through rate
• B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
• C. 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.
• D. Import your user events and then your product catalog to make sure you have the highest quality event stream

Answer: B

Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.

NEW QUESTION 40
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

• A. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
• B. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
• C. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
• D. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery

Answer: C

Explanation:
Google has bought this software and support for this tool is not good. SQL can work in Cloud fusion pipelines too but I would prefer to use a single tool like Bigquery to both transform and store data.

NEW QUESTION 41
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