Pass Guaranteed Quiz 2026 Google GCP-DE: Valid Data Engineer Latest Exam Notes

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Preparing datasets for querying and reporting
  • 2. Optimizing query performance
  • 3. Implementing data sharing and governance
Topic 2: 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
Topic 3: Storing and managing data20%- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Designing data warehouses and data lakes
  • 3. Using data storage services appropriately
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Topic 4: 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. Defining architecture for batch and streaming processing
  • 3. Planning for data migration and integration
Topic 5: Ingesting and processing data25%- Building data pipelines
  • 1. Implementing ingestion mechanisms
  • 2. Handling data quality and consistency
  • 3. Developing batch and streaming workflows
- Transforming data
  • 1. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution

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GCP-DE Reliable Guide Files, GCP-DE Exam Syllabus

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following is NOT true about Dataflow pipelines?

Answer: B

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


NEW QUESTION # 52
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: A


NEW QUESTION # 53
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Answer: A,B,C

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


NEW QUESTION # 54
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

Answer: D


NEW QUESTION # 55
When a Cloud Bigtable node fails, is lost.

Answer: D

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 56
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