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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 20%-25% | - Security and compliance
|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
|
| Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
|
| Designing data processing systems | 22%-27% | - Designing data pipelines
|
Google Data Engineer Sample Questions:
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Create a table on Cloud Spanner, and insert and delete rows with the job information
- B. Create an API using App Engine to receive and send messages to the applications
- C. Create a table on Cloud SQL, and insert and delete rows with the job information
- D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
- A. Data extraction
- B. Source API
- C. ParDo
- D. Sink API
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You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- B. Store the data in Google Cloud Datastor
- C. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- D. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- E. Load the data every 30 minutes into a new partitioned table in BigQuery.
- F. Store the data in a file in a regional Google Cloud Storage bucke
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
- A. BigQuery
- B. Cloud Datastore
- C. Cloud Bigtable
- D. Cloud SQL
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storag
- B. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud SQL for storag
- E. Add secondary indexes to support query patterns.
- F. Use Cloud SQL for storag
- G. Add secondary indexes to support query patterns.
- H. Use Cloud Spanner for storag
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