Google Professional-Data-Engineer Exam Questions 2025 - Instant Access, just revised

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Google Professional-Data-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
Topic 2	Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Topic 3	Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Topic 4	Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
Topic 5	Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.

Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer Exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.

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Google Certified Professional Data Engineer Exam Sample Questions (Q243-Q248):

NEW QUESTION # 243
Scaling a Cloud Dataproc cluster typically involves ____.

A. moving memory to run more applications on a single node
B. deleting applications from unused nodes periodically
C. increasing or decreasing the number of worker nodes
D. increasing or decreasing the number of master nodes

Answer: C

Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1) increase the number of workers to make a job run faster
2) decrease the number of workers to save money
3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage

NEW QUESTION # 244
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
D. Use the NOW () function in BigQuery to record the event's time.

Answer: B

NEW QUESTION # 245
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.
We also need environments in which our data scientists can carefully study and quickly adapt our models.
Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?

A. Create a table called tracking_table with a TIMESTAMP column to represent the day.
B. Create a table called tracking_table and include a DATE column.
C. Create a partitioned table called tracking_table and include a TIMESTAMP column.
D. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Answer: C

NEW QUESTION # 246
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Google Cloud Dataflow job to process the data.
B. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Answer: D

NEW QUESTION # 247
Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query:

How should you redesign the BigQuery table to support faster access?

A. Partition the table by country and username fields.
B. Cluster the table by country field, and partition by username field.
C. Partition the table by _PARTITIONTIME.
D. Cluster the table by country and username fields

Answer: D

Explanation:
To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:
Clustering in BigQuery:
Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.
Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.
Filter Efficiency:
With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.
This directly addresses the performance issue of the dashboard queries that apply filters on these fields.
Steps to Implement:
Redesign the Table:
Create a new table with clustering on countryname and username:
CREATE TABLE project.dataset.new_table
CLUSTER BY countryname, username AS
SELECT * FROM project.dataset.customer_order;
Migrate Data:
Transfer the existing data from the original table to the new clustered table.
Update Queries:
Modify the dashboard queries to reference the new clustered table.
Reference:
BigQuery Clustering Documentation
Optimizing Query Performance

NEW QUESTION # 248
......

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Google Professional-Data-Engineer Exam Questions 2025 - Instant Access, just revised

Google Professional-Data-Engineer Exam Syllabus Topics:

Professional-Data-Engineer Exam Reliable Test Online & High-quality Professional-Data-Engineer Valid Test Tips Pass Success

Google Certified Professional Data Engineer Exam Sample Questions (Q243-Q248):

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