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Google Professional-Data-Engineer certification exam is a rigorous exam designed to test a candidate's skills in designing and implementing data processing systems on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics including data processing, data analysis, data visualization, and machine learning. Professional-Data-Engineer Exam also includes questions on data security, compliance, and privacy. Professional-Data-Engineer exam format includes multiple choice and scenario-based questions, which test a candidate's ability to apply their knowledge in real-world situations.

Google Certified Professional Data Engineer Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
Which Java SDK class can you use to run your Dataflow programs locally?

Answer: B

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun


NEW QUESTION # 135
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

Answer: B,C

Explanation:
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground


NEW QUESTION # 136
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket

Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Answer: C


NEW QUESTION # 137
You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component in order to train and serve the model your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

Answer: D


NEW QUESTION # 138
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

Answer: A


NEW QUESTION # 139
......

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