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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems24%- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Designing for reliability, scalability, and efficiency
  • 3. Selecting appropriate storage solutions
- Designing data pipelines
  • 1. Designing for data transformation and enrichment
  • 2. Planning for data migration and integration
  • 3. Defining architecture for batch and streaming processing
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
Preparing data for analysis and machine learning13%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
- Enabling data analysis
  • 1. Implementing data sharing and governance
  • 2. Preparing datasets for querying and reporting
  • 3. Optimizing query performance
Maintaining and automating data workloads18%- Automation and optimization
  • 1. Automating deployment and management
  • 2. Optimizing resource usage and costs
- Operationalizing workloads
  • 1. Orchestrating and scheduling jobs
  • 2. Ensuring reliability and recoverability
  • 3. Monitoring and troubleshooting pipelines
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. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution

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Google Data Engineer Sample Questions (Q68-Q73):

NEW QUESTION # 68
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

Answer: A


NEW QUESTION # 69
Which of these sources can you not load data into BigQuery from?

Answer: C

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data


NEW QUESTION # 70
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Answer: A


NEW QUESTION # 71
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: A

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


NEW QUESTION # 72
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

Answer: B


NEW QUESTION # 73
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