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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Control operational costs
  • 2. Optimize storage and compute usage
  • 3. Tune query and pipeline performance
- Reliability and scalability
  • 1. Design highly available systems
  • 2. Implement disaster recovery strategies
  • 3. Scale data workloads efficiently
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. Build ETL and ELT pipelines
  • 3. Optimize pipeline performance and cost
Building and operationalizing data processing systems28%-33%- Operationalizing systems
  • 1. Implement fault tolerance and recovery
  • 2. Monitor data pipelines and workloads
  • 3. Manage logging and alerting
- Building data processing systems
  • 1. Implement batch processing systems
  • 2. Implement streaming data systems
  • 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
Ensuring solution quality20%-25%- Security and compliance
  • 1. Implement encryption and compliance controls
  • 2. Protect sensitive data
  • 3. Manage IAM and access control
- Data quality management
  • 1. Maintain metadata and lineage
  • 2. Validate and clean data
  • 3. Implement data governance

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Google Data Engineer Sample Questions (Q41-Q46):

NEW QUESTION # 41
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 # 42
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: B

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


NEW QUESTION # 43
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?

Answer: H

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


NEW QUESTION # 44
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Answer: B

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 45
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?

Answer: C


NEW QUESTION # 46
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