GCP-DE Questions | New GCP-DE Test Vce

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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. Preparing datasets for querying and reporting 3. Optimizing query performance
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management
Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately
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. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
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. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration

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Free PDF Quiz 2026 GCP-DE: Data Engineer – Trustable Questions

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Google Data Engineer Sample Questions (Q69-Q74):

NEW QUESTION # 69
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. before
B. after
C. once
D. only if

Answer: A

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 70
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Spark
B. Blaze
C. Fire
D. Ignite

Answer: A

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION # 71
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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
B. Use K-means Clustering to detect faces in the pixels.
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: A

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 # 72
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Then run the Hadoop utility to copy them do HDF
B. Replicate external Hive tables to the native ones.
C. Replicate external Hive tables to the native ones.
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
F. Mount the Hive tables from HDFS.
G. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
H. Load the ORC files into BigQuer
I. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
J. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
K. Mount the Hive tables locally.
L. Mount the Hive tables locally.

Answer: D,L

NEW QUESTION # 73
Which of these is not a supported method of putting data into a partitioned table?

A. Create a partitioned table and stream new records to it every day.
B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: C

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 74
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