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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems28%-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 solutions20%-25%- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage
- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Implement disaster recovery strategies
  • 3. Design highly available systems
Designing data processing systems22%-27%- Designing for data ingestion
  • 1. Select appropriate storage systems
  • 2. Design scalable and reliable ingestion architecture
  • 3. Design batch and streaming ingestion solutions
- Designing data pipelines
  • 1. Design orchestration workflows
  • 2. Optimize pipeline performance and cost
  • 3. Build ETL and ELT pipelines
Ensuring solution quality20%-25%- Data quality management
  • 1. Implement data governance
  • 2. Validate and clean data
  • 3. Maintain metadata and lineage
- Security and compliance
  • 1. Protect sensitive data
  • 2. Implement encryption and compliance controls
  • 3. Manage IAM and access control

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Google Data Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
What are the minimum permissions needed for a service account used with Google Dataproc?

Answer: D

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes


NEW QUESTION # 36
Which of the following statements is NOT true regarding Bigtable access roles?

Answer: C

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control


NEW QUESTION # 37
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

Answer: E


NEW QUESTION # 38
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

Answer: C


NEW QUESTION # 39
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

Answer: B

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 40
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