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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Defining architecture for batch and streaming processing
  • 2. Designing for data transformation and enrichment
  • 3. Planning for data migration and integration
Topic 2: Ingesting and processing data25%- Building data pipelines
  • 1. Developing batch and streaming workflows
  • 2. Handling data quality and consistency
  • 3. Implementing ingestion mechanisms
- Transforming data
  • 1. Optimizing transformations for performance
  • 2. Applying data processing logic
  • 3. Managing schema evolution
Topic 3: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Preparing datasets for querying and reporting
  • 3. Implementing data sharing and governance
- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation
Topic 4: 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
Topic 5: 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. Orchestrating and scheduling jobs
  • 3. Monitoring and troubleshooting pipelines

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Quiz GCP-DE - Newest Latest Data Engineer Test Pass4sure

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Google Data Engineer Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

Answer: C

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive


NEW QUESTION # 15
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

Answer: C


NEW QUESTION # 16
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?

Answer: A


NEW QUESTION # 17
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Answer: D,E


NEW QUESTION # 18
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

Answer: B

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri


NEW QUESTION # 19
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