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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q40-Q45):

NEW QUESTION # 40
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Answer: D


NEW QUESTION # 41
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

Answer: C

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do


NEW QUESTION # 42
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Answer: C

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers


NEW QUESTION # 43
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

Answer: D


NEW QUESTION # 44
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Answer: F

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


NEW QUESTION # 45
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