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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems24%- Designing data pipelines
  • 1. Defining architecture for batch and streaming processing
  • 2. Designing for data transformation and enrichment
  • 3. Planning for data migration and integration
- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Designing for reliability, scalability, and efficiency
  • 3. Selecting appropriate storage solutions
Topic 2: Maintaining and automating data workloads18%- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
- Operationalizing workloads
  • 1. Orchestrating and scheduling jobs
  • 2. Ensuring reliability and recoverability
  • 3. Monitoring and troubleshooting pipelines
Topic 3: Storing and managing data20%- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Designing data warehouses and data lakes
  • 3. Using data storage services appropriately
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Topic 4: Ingesting and processing data25%- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Developing batch and streaming workflows
  • 3. Implementing ingestion mechanisms
- Transforming data
  • 1. Optimizing transformations for performance
  • 2. Managing schema evolution
  • 3. Applying data processing logic
Topic 5: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Implementing data sharing and governance
  • 2. Optimizing query performance
  • 3. Preparing datasets for querying and reporting
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving

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Google Data Engineer Sample Questions (Q34-Q39):

NEW QUESTION # 34
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

Answer: A


NEW QUESTION # 35
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Answer: D

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 # 36
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: B


NEW QUESTION # 37
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Answer: A


NEW QUESTION # 38
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Answer: B


NEW QUESTION # 39
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