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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Implement encryption and compliance controls 3. Protect sensitive data - Data quality management 1. Validate and clean data 2. Implement data governance 3. Maintain metadata and lineage
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Implement fault tolerance and recovery 2. Monitor data pipelines and workloads 3. Manage logging and alerting - Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design batch and streaming ingestion solutions 2. Design scalable and reliable ingestion architecture 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Implement disaster recovery strategies 2. Scale data workloads efficiently 3. Design highly available systems - Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance

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Quiz 2026 Google Useful GCP-DE: Latest Data Engineer Material

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Google Data Engineer Sample Questions (Q40-Q45):

NEW QUESTION # 40
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
D. Use BigQuery to export the data for the table to a CSV fil
E. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
F. Add a new column called FullName to the Users tabl

Answer: B

NEW QUESTION # 41
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Deploy a Cloud Dataproc cluste
B. Change references in scripts from hdfs:// to gs://
C. Deploy a Cloud Dataproc cluste
D. Use an SSD persistent disk and 50% preemptible worker
E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F. Install the Cloud Storage connector, and store the data in Cloud Storag
G. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
H. Use a standard persistent disk and 50% preemptible worker
I. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
J. Change references in scripts from hdfs:// to gs://
K. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF

Answer: C

NEW QUESTION # 42
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?

A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Create an API using App Engine to receive and send messages to the applications
D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

NEW QUESTION # 43
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?

A. Cloud Datastore
B. Cloud Bigtable
C. Cloud Spanner
D. Cloud SQL

Answer: D

NEW QUESTION # 44
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Train your own image recognition model leveraging transfer learning techniques.
B. Use Cloud Vision API by providing custom labels as recognition hints.
C. Use Cloud Vision AutoML, but reduce your dataset twice.
D. Use Cloud Vision AutoML with the existing dataset.

Answer: D

NEW QUESTION # 45
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