GCP-DE Reliable Test Syllabus, Preparation GCP-DE Store

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q30-Q35):

NEW QUESTION # 30
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: C

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 # 31
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 # 32
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

Answer: C

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 33
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

Answer: A


NEW QUESTION # 34
Cloud Dataproc is a managed Apache Hadoop and Apache service.

Answer: D

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/


NEW QUESTION # 35
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