Google GCP-DE Questions - To Pass Exam Easily [2026]

Getting the Data Engineer (GCP-DE) certification exam is necessary in order to get a job in your desired tech company. Success in the Data Engineer certification exam gives you an edge over the others because you will have certified skills. The Data Engineer (GCP-DE) certification exam badge will make a good impression on the interviewer. Most of the people planning to attempt the Data Engineer (GCP-DE) exam are confused that how will they prepare and pass Data Engineer (GCP-DE) exam with good grades.

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Implementing data sharing and governance 2. Optimizing query performance 3. Preparing datasets for querying and reporting
Topic 2: Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Designing data warehouses and data lakes 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Topic 3: Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Selecting appropriate storage solutions 3. Planning for data security and compliance - Designing data pipelines 1. Designing for data transformation and enrichment 2. Planning for data migration and integration 3. Defining architecture for batch and streaming processing
Topic 4: Ingesting and processing data	25%	- 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
Topic 5: Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines

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Google Data Engineer Sample Questions (Q60-Q65):

NEW QUESTION # 60
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Build and train a text classification model using TensorFlo
B. Process the generated Entity Analysis as labels.
C. Call the model from your application and process the results as labels.
D. Build and train a text classification model using TensorFlo
E. Call the Cloud Natural Language API from your applicatio
F. Call the Cloud Natural Language API from your applicatio
G. Call the model from your application and process the results as labels.
H. Deploy the model using a KubernetesEngine cluste
I. Deploy the model using Cloud Machine Learning Engin
J. Process the generated Sentiment Analysis as labels.

Answer: B

NEW QUESTION # 61
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. HiveQL using Hive
B. Python using MapReduce
C. Java using MapReduce
D. PigLatin using Pig

Answer: B

NEW QUESTION # 62
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger that is a combination of other triggers
B. Trigger based on time
C. Trigger based on element count
D. Trigger based on element size in bytes

Answer: D

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 # 63
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

NEW QUESTION # 64
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
B. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Answer: B

NEW QUESTION # 65
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GCP-DE Lab Questions: https://www.latestcram.com/GCP-DE-exam-cram-questions.html