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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Design highly available systems
  • 3. Implement disaster recovery strategies
- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage
Topic 2: 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. Design scalable and reliable ingestion architecture
  • 2. Select appropriate storage systems
  • 3. Design batch and streaming ingestion solutions
Topic 3: 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. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 2. Implement streaming data systems
  • 3. Implement batch processing systems
Topic 4: Ensuring solution quality20%-25%- Data quality management
  • 1. Maintain metadata and lineage
  • 2. Validate and clean data
  • 3. Implement data governance
- Security and compliance
  • 1. Implement encryption and compliance controls
  • 2. Manage IAM and access control
  • 3. Protect sensitive data

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Google Data Engineer Sample Questions (Q65-Q70):

NEW QUESTION # 65
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

Answer: A,C


NEW QUESTION # 66
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

Answer: D

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive


NEW QUESTION # 67
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?

Answer: D


NEW QUESTION # 68
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Answer: A


NEW QUESTION # 69
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: A

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 # 70
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