Updated: Aug 20, 2026
No. of Questions: 77 Questions & Answers with Testing Engine
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| Certification Vendor: | Google Cloud |
| Exam Name: | Professional Data Engineer |
| Exam Number: | GCP-DE |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Japanese |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple choice, Multiple select |
| Real Exam Qty: | 50-60 |
| Exam Price: | $200 USD |
| Sample Questions: | Google GCP-DE Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No formal prerequisites. Google recommends 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing data processing systems | 22%-27% | - Designing data pipelines
|
| Topic 2: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
|
| Topic 3: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
|
| Topic 4: Ensuring solution quality | 20%-25% | - Security and compliance
|
1. Which is not a valid reason for poor Cloud Bigtable performance?
A) There are issues with the network connection.
B) The workload isn't appropriate for Cloud Bigtable.
C) The Cloud Bigtable cluster has too many nodes.
D) The table's schema is not designed correctly.
2. As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
A) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
B) Introduce resource hierarchy to leverage access control policy inheritance.
C) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
D) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
F) Use Cloud Deployment Manager to automate access provision.
3. 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) Use Cloud Dataproc to run your transformation
B) Monitor the total execution time for a sampling of job
C) Configure the job to use non-default Compute Engine machine types when needed.
D) Monitor the job system lag with Stackdrive
E) Use Cloud Dataproc to run your transformation
F) Use Cloud Dataflow to run your transformation
G) Monitor CPU utilization for the cluste
H) Use the default autoscaling setting for worker instances.
I) Resize the number of worker nodes in your cluster via the command line.
J) Locate the bottleneck and adjust cluster resources.
K) Use the diagnose command to generate an operational output archiv
L) Use Cloud Dataflow to run your transformation
4. 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) Load the ORC files into BigQuer
B) Mount the Hive tables locally.
C) Then run the Hadoop utility to copy them do HDF
D) Mount the Hive tables locally.
E) Mount the Hive tables from HDFS.
F) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
G) Replicate external Hive tables to the native ones.
H) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
I) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
J) Replicate external Hive tables to the native ones.
K) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
L) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
5. 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 feature engineering to add features for eyes, noses, and mouths to the input data.
B) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
C) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D) Use K-means Clustering to detect faces in the pixels.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B,D | Question # 3 Answer: G | Question # 4 Answer: D,F | Question # 5 Answer: C |
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