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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

Answer: C

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu


NEW QUESTION # 16
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

Answer: B

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model


NEW QUESTION # 17
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 # 18
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Answer: A


NEW QUESTION # 19
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

Answer: C

Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 20
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