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Google Professional Data Engineer (GCP-PDE) Exam | Tips & Tricks

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Google Professional Data Engineer (GCP-PDE) Exam | Tips & Tricks

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  1. How to Prepare for Google Professional Data Engineer Certification? Google GCP-PDE Certification Made Easy with VMExam.com.

  2. GCP-PDE Professional Data Engineer Certification Details Exam Code GCP-PDE Full Exam Name Google Cloud Platform - Professional Data Engineer (GCP-PDE) 40-50 No. of Questions Online Practice Exam Google GCP-PDE Practice Test Sample Questions Google GCP-PDE Sample Questions Passing Score Pass / Fail (Approx 70%) Time Limit 120 minutes Exam Fees $200 USD Become successful with VMExam.com

  3. Google GCP-PDE Study Guide • Perform enough practice with related Professional Data Engineer certification on VMExam.com. • Understand the Exam Topics very well. • Identify your weak areas from practice test and do more practice with VMExam.com. Become successful with VMExam.com

  4. Google GCP-PDE Certification Syllabus Syllabus Topics ● Designing data processing systems (~22% of the exam) ● Ingesting and processing the data (~25% of the exam) ● Storing the data (~20% of the exam) ● Preparing and using data for analysis (~15% of the exam) ● Maintaining and automating data workloads (~18% of the exam) Become successful with VMExam.com

  5. Professional Data Engineer Training Details Training: Google Cloud training Google Cloud documentation Google Cloud solutions Become successful with VMExam.com

  6. Google GCP-PDE Sample Questions Become successful with VMExam.com

  7. Que.01: You used a small amount of data to build a machine learning model that gives you good inferences during testing. However, the results show more errors when real-world data is used to run the model. No additional data can be collected for testing. You want to get a more accurate view of the model's capability. What should you do? Options: a) Reduce the amount of data to improve the model. b) Cross-validate the data, and re-run the model building process. c) Create feature crosses that will add new columns to increase the data. d) Duplicate the data twice to increase the data, and re-run the model building process. Become successful with VMExam.com

  8. Answer b) Cross-validate the data, and re-run the model building process. Become successful with VMExam.com

  9. Que.02: Your cryptocurrency trading company visualizes prices to help your customers make trading decisions. Because different trades happen in real time, the price data is fed to a data pipeline that uses Dataflow for processing. You want to compute moving averages. What should you do? Options: a) Use session windows in Dataflow. b) Use hopping windows in Dataflow. c) Use tumbling windows in Dataflow. d) Use Dataflow SQL, and compute averages grouped by time. Become successful with VMExam.com

  10. Answer b) Use hopping windows in Dataflow. Become successful with VMExam.com

  11. Que.03: Your Bigtable database was recently deployed into production. The scale of data ingested and analyzed has increased significantly, but the performance has degraded. You want to identify the performance issue. What should you do? Options: a) Use Key Visualizer to analyze performance. b) Use Cloud Trace to identify the performance issue. c) Add logging statements into the code to see which inserts cause the delay. d) Add more nodes to the cluster to see if that resolves the performance issue. Become successful with VMExam.com

  12. Answer a) Use Key Visualizer to analyze performance. Become successful with VMExam.com

  13. Que.04: You are building the trading platform for a stock exchange with millions of traders. Trading data is written rapidly. You need to retrieve data quickly to show visualizations to the traders, such as the changing price of a particular stock over time. You need to choose a storage solution in Google Cloud. What should you do? Options: a) Use Memorystore. b) Use Firestore. c) Use Cloud SQL. d) Use Bigtable. Become successful with VMExam.com

  14. Answer d) Use Bigtable. Become successful with VMExam.com

  15. Que.05: You are working on optimizing BigQuery for a query that is run repeatedly on a single table. The data queried is about 1 GB, and some rows are expected to change about 10 times every hour. You have optimized the SQL statements as much as possible. You want to further optimize the query's performance. What should you do? Options: a) Create a materialized view based on the table, and query that view. b) Enable caching of the queried data so that subsequent queries are faster. c) Create a scheduled query, and run it a few minutes before the report has to be created. d) Reserve a larger number of slots in advance so that you have maximum compute power to execute the query. Become successful with VMExam.com

  16. Answer a) Create a materialized view based on the table, and query that view. Become successful with VMExam.com

  17. Google Professional Data Engineer Certification Guide • The Google Certification is increasingly becoming important for the career of employees. • Try our Professional Data Engineer mock test. Become successful with VMExam.com

  18. More Info on Google Certification Visit www.vmexam.com Become successful with VMExam.com

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