100% Pass 2026 Reliable Google GCP-DE: Free Data Engineer Exam Questions

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Storing and managing data	20%	- 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. Managing data lifecycle and retention 3. Designing data warehouses and data lakes
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows
Designing data processing systems	24%	- 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. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 10 minutes
B. Windows at every 100 MB of data
C. Single, Global Window
D. Windows at every 1 minute

Answer: C

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 67
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
B. In the event of an emergency, use a point-in-time snapshot to recover the data.
C. In the event of an emergency, use the backup copy of the table.
D. Set the BigQuery dataset to be multi-regional
E. In the event of an emergency, use the backup copy of the table.
F. Set the BigQuery dataset to be regiona
G. In the event of an emergency, use a point-in-time snapshot to recover the data.
H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
I. Set the BigQuery dataset to be regiona
J. Set the BigQuery dataset to be multi-regional

Answer: B

NEW QUESTION # 68
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
B. The CSV data has invalid rows that were skipped on import.
C. The CSV data has not gone through an ETL phase before loading into BigQuery.
D. The CSV data loaded in BigQuery is not flagged as CSV.

Answer: B

NEW QUESTION # 69
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. Add a new column called FullName to the Users tabl
B. 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.
C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
E. 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.
F. Use BigQuery to export the data for the table to a CSV fil

Answer: C

NEW QUESTION # 70
Which is not a valid reason for poor Cloud Bigtable performance?

A. The table's schema is not designed correctly.
B. The Cloud Bigtable cluster has too many nodes.
C. There are issues with the network connection.
D. The workload isn't appropriate for Cloud Bigtable.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 71
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