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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Preparing data for analysis and machine learning13%- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation
- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Preparing datasets for querying and reporting
  • 3. Implementing data sharing and governance
Designing data processing systems24%- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability, scalability, and efficiency
- Designing data pipelines
  • 1. Planning for data migration and integration
  • 2. Designing for data transformation and enrichment
  • 3. Defining architecture for batch and streaming processing
Maintaining and automating data workloads18%- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
- Operationalizing workloads
  • 1. Ensuring reliability and recoverability
  • 2. Monitoring and troubleshooting pipelines
  • 3. Orchestrating and scheduling jobs
Storing and managing data20%- Optimizing storage performance and cost
  • 1. Implementing cost optimization strategies
  • 2. Configuring storage for access patterns
- Implementing storage solutions
  • 1. Using data storage services appropriately
  • 2. Designing data warehouses and data lakes
  • 3. Managing data lifecycle and retention
Ingesting and processing data25%- Transforming data
  • 1. Optimizing transformations for performance
  • 2. Applying data processing logic
  • 3. Managing schema evolution
- Building data pipelines
  • 1. Implementing ingestion mechanisms
  • 2. Handling data quality and consistency
  • 3. Developing batch and streaming workflows

>> GCP-DE最新考證 <<

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最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q53-Q58):

問題 #53
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

答案:B,C

解題說明:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms


問題 #54
Which of the following statements is NOT true regarding Bigtable access roles?

答案:D

解題說明:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control


問題 #55
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?

答案:B


問題 #56
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

答案:B


問題 #57
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

答案:G


問題 #58
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