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Google Professional-Data-Engineer Exam is intended for professionals who work with data engineering, data integration, or data analysis. Professional-Data-Engineer exam tests the candidate's knowledge and understanding of Google Cloud Platform tools and services, including BigQuery, Cloud Dataflow, Cloud Pub/Sub, Cloud Storage, and more. Professional-Data-Engineer exam consists of multiple-choice questions and practical scenarios that test the candidate's ability to apply their knowledge and skills to real-world problems. Passing the exam and obtaining the certification demonstrates the individual's proficiency in designing and implementing scalable and reliable data processing systems using Google Cloud Platform technologies.
The following will be discussed here:
NEW QUESTION # 111
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?
Answer: C
NEW QUESTION # 112
Case Study: 1 - Flowlogistic
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 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 wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
Answer: C
NEW QUESTION # 113
You need to orchestrate a pipeline with several Google Cloud services: a batch Dataflow job, then a BigQuery query job followed by a Vertex AI batch prediction. The logic is sequential. You want a lightweight, serverless orchestration solution with minimal operational overhead. What service should you use?
Answer: A
Explanation:
Cloud Workflows is a fully serverless orchestration service designed to coordinate sequential calls to Google Cloud services and APIs. It is lightweight, requires no infrastructure management, supports step-by-step execution with built-in error handling and retries, and is ideal for orchestrating simple, sequential pipelines involving services such as Dataflow, BigQuery, and Vertex AI.
NEW QUESTION # 114
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.
Answer: A,D
NEW QUESTION # 115
You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?
Answer: B
Explanation:
The key requirements for the windowing strategy are:
A window groups data for a specific sensor.
A window should contain data spanningat least30 minutes ("duration of more than 30 minutes" implies activity for this period).
A window for a sensorendswhen no data has been received from that sensor for 15 minutes (this is a gap).
This scenario perfectly describessession windows.
Session Windows:Session windows group elements (per key, e.g., per sensor ID) that arrive within a certain
"gap duration" of each other. A new session starts if data for a key arrives after the gap duration has passed since the last data point for that key.
In this case, if data stops arriving for a sensor for 15 minutes, the current session for that sensor closes. This matches "the window ends when no data has been received for 15 minutes." The "duration of more than 30 minutes" requirement is a condition you would applyafterthe session window closes. You'd calculate the duration of the data within the closed session window and only compute the average if that session's duration (span of event times within it) exceeds 30 minutes. Session windows themselves don't have a fixed duration; their duration is determined by data activity and the gap.
Let's analyze why other options are less suitable:
A (Hopping windows with a 15-minute window, and a thirty-minute period):Hopping windows have a fixed size and a fixed period. They create overlapping windows. This doesn't align with the dynamic nature of sessions ending based on inactivity. A 30-minute period with a 15-minute window means windows like [0:00-
0:15], [0:15-0:30], [0:30-0:45]. If activity is continuous, a 30-minute activity span would be covered, but the window closing is not based on a 15-minute gap of inactivity.
B (Tumbling windows with a 15-minute window and a fifteen-minute .withAllowedLateness operator):
Tumbling windows are fixed-size, non-overlapping windows. .withAllowedLateness deals with late data arriving for a window that has already passed its end time, not with defining the window based on activity gaps.
C (Session windows with a 30-minute gap duration):This would mean a session ends only if there's a 30- minute gap of inactivity. The requirement is a 15-minute gap.
Therefore, session windows with a 15-minute gap duration (Option D) correctly model the requirement for windows to close after 15 minutes of inactivity from a sensor. The subsequent filtering for sessions lasting more than 30 minutes is a downstream operation.
Reference:
Apache Beam Programming Guide > Windowing > Windowing functions > Session windows. "Session windowing assigns elements to windows that represent sessions of activity. A session window starts when the first element arrives for a key. If another element arrives for that key within the specified gap duration, that element is included in the existing session window. If an element arrives after the gap duration, a new session window starts for that element... Session windows are useful for data that is irregularly distributed with respect to time, such as user activity data." This directly matches the sensor data behavior: data arrives when noise is high, and a period of no data for 15 minutes should close the analysis window for that sensor.
NEW QUESTION # 116
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