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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems22%-27%- Designing data pipelines
  • 1. Optimize pipeline performance and cost
  • 2. Build ETL and ELT pipelines
  • 3. Design orchestration workflows
- Designing for data ingestion
  • 1. Select appropriate storage systems
  • 2. Design scalable and reliable ingestion architecture
  • 3. Design batch and streaming ingestion solutions
Topic 2: Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 2. Implement batch processing systems
  • 3. Implement streaming data systems
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Implement fault tolerance and recovery
  • 3. Monitor data pipelines and workloads
Topic 3: Ensuring solution quality20%-25%- Data quality management
  • 1. Maintain metadata and lineage
  • 2. Validate and clean data
  • 3. Implement data governance
- Security and compliance
  • 1. Manage IAM and access control
  • 2. Protect sensitive data
  • 3. Implement encryption and compliance controls
Topic 4: Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Design highly available systems
  • 2. Scale data workloads efficiently
  • 3. Implement disaster recovery strategies
- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage

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Google Data Engineer Sample Questions (Q45-Q50):

NEW QUESTION # 45
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

Answer: A


NEW QUESTION # 46
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

Answer: A


NEW QUESTION # 47
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri


NEW QUESTION # 48
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Answer: B


NEW QUESTION # 49
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: A

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection


NEW QUESTION # 50
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