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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Answer: B


NEW QUESTION # 74
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


NEW QUESTION # 75
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: D


NEW QUESTION # 76
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: C,D


NEW QUESTION # 77
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

Answer: D


NEW QUESTION # 78
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