Google GCP-DE

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question #1
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 SQL, and insert and delete rows with the job information
B. Create a table on Cloud Spanner, and insert and delete rows with the job information
C. Create an API using App Engine to receive and send messages to the applications
D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Question #2
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Question #3
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "city.person".
B. Change "person" to "person.city".
C. Add ", UNNEST(city)" before the WHERE clause.
D. Add ", UNNEST(person)" before the WHERE clause.

Question #4
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Split table into multiple tables; 2) Use a partitioned table
D. 1) Join tables into one table; 2) Use nested repeated fields

Question #5
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Convert the sharded tables into a single partitioned table
B. Enable query caching so you can cache data from previous months
C. Convert all daily log tables into date-partitioned tables
D. Create separate views to cover each month, and query from these views

Solutions: