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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 2. Implement batch processing systems
  • 3. Implement streaming data systems
- Operationalizing systems
  • 1. Monitor data pipelines and workloads
  • 2. Implement fault tolerance and recovery
  • 3. Manage logging and alerting
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. Implement data governance
  • 3. Validate and clean data
Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Control operational costs
  • 2. Tune query and pipeline performance
  • 3. Optimize storage and compute usage
- Reliability and scalability
  • 1. Design highly available systems
  • 2. Implement disaster recovery strategies
  • 3. Scale data workloads efficiently
Designing data processing systems22%-27%- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Optimize pipeline performance and cost
  • 3. Design orchestration workflows
- Designing for data ingestion
  • 1. Select appropriate storage systems
  • 2. Design scalable and reliable ingestion architecture
  • 3. Design batch and streaming ingestion solutions

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Google Data Engineer Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which is not a valid reason for poor Cloud Bigtable performance?

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance


NEW QUESTION # 25
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

Answer: A


NEW QUESTION # 26
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers


NEW QUESTION # 27
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: B

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection


NEW QUESTION # 28
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Answer: E


NEW QUESTION # 29
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