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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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 an API using App Engine to receive and send messages to the applications
B) Create a table on Cloud Spanner, and insert and delete rows with the job information
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

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

3. 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) BigQuery
B) Cloud Datastore
C) Cloud BigTable
D) Cloud SQL

4. What are all of the BigQuery operations that Google charges for?

A) Storage, queries, and streaming inserts
B) Storage, queries, and exporting data
C) Storage, queries, and loading data from a file
D) Queries and streaming inserts

5. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use Cloud Dataflow to run your transformation
B) Use the default autoscaling setting for worker instances.
C) Use Cloud Dataproc to run your transformation
D) Monitor the job system lag with Stackdrive
E) Monitor CPU utilization for the cluste
F) Use Cloud Dataproc to run your transformation
G) Configure the job to use non-default Compute Engine machine types when needed.
H) Resize the number of worker nodes in your cluster via the command line.
I) Monitor the total execution time for a sampling of job
J) Locate the bottleneck and adjust cluster resources.
K) Use the diagnose command to generate an operational output archiv
L) Use Cloud Dataflow to run your transformation

Solutions: