2026 Google GCP-DE: Efficient New Data Engineer Test Tutorial

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
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. Change references in scripts from hdfs:// to gs://
B. Change references in scripts from hdfs:// to gs://
C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
E. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
F. Use a standard persistent disk and 50% preemptible worker
G. Deploy a Cloud Dataproc cluste
H. Use an SSD persistent disk and 50% preemptible worker
I. Install the Cloud Storage connector, and store the data in Cloud Storag
J. Deploy a Cloud Dataproc cluste
K. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance

Answer: G

NEW QUESTION # 34
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B. Merge the transformed tables together with a SQL query
C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Answer: D

NEW QUESTION # 35
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create an additional table with only the necessary columns.
B. Export the data into a Google Sheet for virtualization.
C. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
D. Create a view on the table to present to the virtualization tool.

Answer: D

NEW QUESTION # 36
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. The with Allowed Lateness method
B. An hourly watermark
C. A processing time trigger
D. An event time trigger

Answer: C

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

NEW QUESTION # 37
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?

A. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud SQL for storag
D. Use Cloud Spanner for storag
E. Use Cloud SQL for storag
F. Add secondary indexes to support query patterns.
G. Use Cloud Spanner for storag
H. Add secondary indexes to support query patterns.

Answer: A

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

NEW QUESTION # 38
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