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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
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. Managing schema evolution
  • 2. Applying data processing logic
  • 3. Optimizing transformations for performance
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. Supporting model training and serving
  • 2. Feature engineering and data preparation
Designing data processing systems24%- Planning data solutions
  • 1. Designing for reliability, scalability, and efficiency
  • 2. Selecting appropriate storage solutions
  • 3. Planning for data security and compliance
- Designing data pipelines
  • 1. Defining architecture for batch and streaming processing
  • 2. Planning for data migration and integration
  • 3. Designing for data transformation and enrichment
Storing and managing data20%- Implementing storage solutions
  • 1. Using data storage services appropriately
  • 2. Designing data warehouses and data lakes
  • 3. Managing data lifecycle and retention
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Maintaining and automating data workloads18%- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
- Operationalizing workloads
  • 1. Monitoring and troubleshooting pipelines
  • 2. Orchestrating and scheduling jobs
  • 3. Ensuring reliability and recoverability

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Google Data Engineer Sample Questions (Q61-Q66):

NEW QUESTION # 61
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: D

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 # 62
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Answer: B


NEW QUESTION # 63
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

Answer: A


NEW QUESTION # 64
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Answer: D


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: B,C


NEW QUESTION # 66
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