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Standard Professional-Data-Engineer Answers, New Professional-Data-Engineer Exam Experience

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Google Certified Professional Data Engineer Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
B. Create a numeric column from a feature cross of latitude and longtitude.
C. Provide latitude and longtitude as input vectors to your neural net.
D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

Answer: B

Explanation:
Reference https://cloud.google.com/bigquery/docs/gis-dataa
To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice:
* Feature Crosses:
* Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.
* This approach allows the neural network to learn complex patterns related to geographic location more effectively than using raw latitude and longitude values.
* Numerical Representation:
* Converting the feature cross into a numeric column simplifies the input for the neural network and can improve the model's ability to learn from the data.
* This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.
* Model Training:
* Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.

NEW QUESTION # 50
An online retailer has built their current application on Google App Engine. A new initiative at the company
mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using
a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google
Cloud database should they choose?

A. Cloud BigTable
B. Cloud SQL
C. Cloud Datastore
D. BigQuery

Answer: A

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 51
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?

A. The CSV data loaded in BigQuery is not flagged as CSV.
B. The CSV data has invalid rows that were skipped on import.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: B

NEW QUESTION # 52
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
B. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
C. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used
D. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Answer: B

NEW QUESTION # 53
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

A. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
B. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
D. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Answer: D

Explanation:
Explanation

NEW QUESTION # 54
......

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