New Release GCP-DE PDF Dumps [2026] - GCP-DE Data Engineer Exam Questions

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. Data encryption and compliance 2. IAM and access control
Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability

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Google Data Engineer Sample Questions (Q47-Q52):

NEW QUESTION # 47
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use K-means Clustering to detect faces in the pixels.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: B

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 48
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Create a temporary table in Cloud Spanner that will act as a buffer for new data
B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
C. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
D. Use a larger instance type for your Cloud Dataflow workers
E. Increase the number of max workers
F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G. Create a temporary table in Cloud Bigtable that will act as a buffer for new data

Answer: C,D

NEW QUESTION # 49
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Resize the number of worker nodes in your cluster via the command line.
B. Use Cloud Dataproc to run your transformation
C. Monitor the total execution time for a sampling of job
D. Use the diagnose command to generate an operational output archiv
E. Use Cloud Dataflow to run your transformation
F. Use the default autoscaling setting for worker instances.
G. Use Cloud Dataflow to run your transformation
H. Use Cloud Dataproc to run your transformation
I. Monitor the job system lag with Stackdrive
J. Monitor CPU utilization for the cluste
K. Locate the bottleneck and adjust cluster resources.
L. Configure the job to use non-default Compute Engine machine types when needed.

Answer: J

NEW QUESTION # 50
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 element count
C. Trigger based on element size in bytes
D. Trigger based on time

Answer: C

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 # 51
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Hive
B. Pig
C. YARN
D. Spark

Answer: A,B,D

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 # 52
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