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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery - Building data processing systems 1. Implement batch processing systems 2. Implement streaming data systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Design highly available systems 2. Scale data workloads efficiently 3. Implement disaster recovery strategies - Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Select appropriate storage systems 3. Design batch and streaming ingestion solutions - Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines
Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Protect sensitive data 3. Manage IAM and access control - Data quality management 1. Implement data governance 2. Maintain metadata and lineage 3. Validate and clean data

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. Cloud Datastore
B. Cloud SQL
C. Cloud Bigtable
D. BigQuery

Antwort: B

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

Antwort: A

Begründung:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

14. Frage
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D. Create a cron schedule in Cloud Dataprep.

Antwort: B

15. Frage
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A. Increase the size of the Google Persistent Disk on your server.
B. Increase your network bandwidth from Compute Engine to Cloud Storage.
C. Increase your network bandwidth from your datacenter to GCP.
D. Increase the CPU size on your server.

Antwort: C

16. Frage
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. Introduce resource hierarchy to leverage access control policy inheritance.
D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E. Use Cloud Deployment Manager to automate access provision.
F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Antwort: C,D

17. Frage
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