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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality20%-25%- Security and compliance
  • 1. Manage IAM and access control
  • 2. Implement encryption and compliance controls
  • 3. Protect sensitive data
- Data quality management
  • 1. Implement data governance
  • 2. Maintain metadata and lineage
  • 3. Validate and clean data
Topic 2: Designing data processing systems22%-27%- Designing for data ingestion
  • 1. Design batch and streaming ingestion solutions
  • 2. Design scalable and reliable ingestion architecture
  • 3. Select appropriate storage systems
- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Design orchestration workflows
  • 3. Optimize pipeline performance and cost
Topic 3: 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. Implement fault tolerance and recovery
  • 2. Monitor data pipelines and workloads
  • 3. Manage logging and alerting
Topic 4: Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Optimize storage and compute usage
  • 2. Control operational costs
  • 3. Tune query and pipeline performance
- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Design highly available systems
  • 3. Implement disaster recovery strategies

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Google Data Engineer Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Answer: B

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 59
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: C


NEW QUESTION # 60
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Answer: B


NEW QUESTION # 61
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 # 62
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,D


NEW QUESTION # 63
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