Google Professional-Data-Engineer软件版 - Professional-Data-Engineer考試指南

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在你的職業生涯中，你正面臨著挑戰嗎？你想提高自己的技能更好地向別人證明你自己嗎？你想得到更多的機會晉升嗎？那麼快報名參加IT認證考試獲得認證資格吧。Google的認證考試是IT領域很重要的考試之一，如果獲得Google的認證資格，那麼你就可以得到很大的幫助。你可以先從通過Professional-Data-Engineer認證考試開始，因為這是Google的一個非常重要的考試。那麼，想知道怎麼快速地通過考試嗎？NewDumps的考試資料可以幫助你達到自己的目標。

獲得 Google Professional-Data-Engineer 認證可以為數據工程行業的專業人士提供競爭優勢。它展示了他們對 GCP 數據工程服務的掌握和他們設計、構建和維護高效數據處理系統的能力。此外，認證還可以帶來增加的就業機會和更高的薪水。

Google專業數據工程師（Google認證專業數據工程師）認證考試是針對在Google Cloud平台上設計、構建和管理數據處理系統的專業人士設計的。該考試適用於希望驗證其數據工程技能和知識的專業人士，包括可擴展和堅固的數據處理系統的設計和實施。

要通過Google Professional-Data-Engineer考試，候選人必須具有穩固的資料工程概念和技術理解，以及實際在Google Cloud平台上工作的經驗。他們必須能夠設計和實施安全、可擴展和高效的資料處理系統，並具有按需進行故障排除和優化這些系統的能力。該考試是具有挑戰性和綜合性的，但通過該考試可以為那些有興趣與Google Cloud平台合作的資料工程人員開啟許多職業機會。

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Professional-Data-Engineer考試指南 - Professional-Data-Engineer題庫最新資訊

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最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q198-Q203):

問題 #198
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub
/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

答案：D

解題說明：
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last
30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low- latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.


問題 #199
Which of these statements about exporting data from BigQuery is false?

答案：A

解題說明：
Explanation
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
Reference: https://cloud.google.com/bigquery/docs/exporting-data


問題 #200
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

答案：C

解題說明：
Explanation/Reference:
Reference https://cloud.google.com/bigquery/docs/gis-data


問題 #201
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?

答案：B


問題 #202
You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using Sidelnputs to join data You noticed that the pipeline is taking longer to complete than expected, what should you do to expedite the Dataflow job?

答案：D


問題 #203
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