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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q29-Q34):

NEW QUESTION # 29
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

Answer: A


NEW QUESTION # 30
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Answer: C


NEW QUESTION # 31
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

Answer: C


NEW QUESTION # 32
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: B


NEW QUESTION # 33
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

Answer: D


NEW QUESTION # 34
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