GCP-DE Latest Exam Pdf, GCP-DE New Questions

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
Which of the following is NOT true about Dataflow pipelines?

Answer: C

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


NEW QUESTION # 67
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

Answer: A


NEW QUESTION # 68
When a Cloud Bigtable node fails, is lost.

Answer: B

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 69
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

Answer: D

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 # 70
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Answer: F

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


NEW QUESTION # 71
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