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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems24%- Designing data pipelines
  • 1. Defining architecture for batch and streaming processing
  • 2. Planning for data migration and integration
  • 3. Designing for data transformation and enrichment
- Planning data solutions
  • 1. Designing for reliability, scalability, and efficiency
  • 2. Selecting appropriate storage solutions
  • 3. Planning for data security and compliance
Topic 2: Ingesting and processing data25%- Transforming data
  • 1. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution
- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Implementing ingestion mechanisms
  • 3. Developing batch and streaming workflows
Topic 3: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Monitoring and troubleshooting pipelines
  • 2. Ensuring reliability and recoverability
  • 3. Orchestrating and scheduling jobs
- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
Topic 4: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Implementing data sharing and governance
  • 2. Preparing datasets for querying and reporting
  • 3. Optimizing query performance
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
Topic 5: Storing and managing data20%- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
- Implementing storage solutions
  • 1. Designing data warehouses and data lakes
  • 2. Using data storage services appropriately
  • 3. Managing data lifecycle and retention

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Google Data Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which is not a valid reason for poor Cloud Bigtable performance?

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance


NEW QUESTION # 16
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: B

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 # 17
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

Answer: C


NEW QUESTION # 18
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

Answer: A,C


NEW QUESTION # 19
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Answer: A

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


NEW QUESTION # 20
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