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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Implement batch processing systems
  • 2. Implement streaming data systems
  • 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Monitor data pipelines and workloads
  • 3. Implement fault tolerance and recovery
Ensuring solution quality20%-25%- Data quality management
  • 1. Implement data governance
  • 2. Maintain metadata and lineage
  • 3. Validate and clean data
- Security and compliance
  • 1. Manage IAM and access control
  • 2. Implement encryption and compliance controls
  • 3. Protect sensitive data
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. Implement disaster recovery strategies
  • 2. Scale data workloads efficiently
  • 3. Design highly available systems
Designing data processing systems22%-27%- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Design orchestration workflows
  • 3. Optimize pipeline performance and cost
- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Select appropriate storage systems
  • 3. Design batch and streaming ingestion solutions

Google Data Engineer Sample Questions:

Question #1

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

  • A. Use the WHERE_PARTITIONTIME pseudo column
  • B. Use the TABLE_DATE_RANGE function
  • C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
  • D. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

  • A. Cloud Speech-to-Text API
  • B. Dialogflow Enterprise Edition
  • C. Cloud Natural Language API
  • D. Cloud AutoML Natural Language
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

  • A. Create an additional table with only the necessary columns.
  • B. Create a view on the table to present to the virtualization tool.
  • C. Export the data into a Google Sheet for virtualization.
  • D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

Which Google Cloud Platform service is an alternative to Hadoop with Hive?

  • A. BigQuery
  • B. Cloud Dataflow
  • C. Cloud Datastore
  • D. Cloud Bigtable
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

  • A. Convert the sharded tables into a single partitioned table
  • B. Create separate views to cover each month, and query from these views
  • C. Convert all daily log tables into date-partitioned tables
  • D. Enable query caching so you can cache data from previous months
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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