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Professional-Data-Engineer資料 &新版Professional-Data-Engineer題庫上線

KaoGuTi有強大的專家團隊不斷為你提供有效的培訓資源，他們不斷利用他們的豐富的經驗和知識研究過去幾年的試題。終於KaoGuTi的有針對性的練習題和答案問世了，它們對很多參加IT認證考試的人起到了很大的幫助。你現在在網上可以免費下載KaoGuTi提供的部分關於Google Professional-Data-Engineer認證考試的模擬測試題和答案作為嘗試。通過很多IT專業人士的使用證明KaoGuTi很可靠。一般如果你使用KaoGuTi提供的針對性復習題，你可以100%通過Google Professional-Data-Engineer 認證考試。快將KaoGuTi加入你的購物車吧! 下一個IT行業的成功人士說不定就是你。

為了準備考試，候選人可以參加官方的GCP培訓課程或利用各種線上資源，例如練習考試、範例問題和學習材料。擁有實際運用Google Cloud技術的實務經驗是成功通過考試的必備條件。該考試為期兩個小時，及格分數為70％。一旦候選人完成考試並獲得證書，他們就可以獲得專業社交網絡的獨家機會，並成為數據工程師行業專家的認可。

Google 專業數據工程師考試涵蓋了眾多主題，包括了解 Google Cloud 平台用於存儲、處理和分析數據、設計數據處理系統、數據建模、數據安全和合規性。此外，考試還測試候選人在 Google Cloud 平台上實施數據管道、數據轉換和處理以及機器學習模型的知識。通過此考試，表明候選人擁有設計和構建數據處理系統的技能和知識，以滿足業務需求並在 Google Cloud 平台上有效擴展。

實現Google Professional-DATA工程師認證是數據專業人員證明其專業知識並提高職業生涯的絕佳方式。該認證受到雇主的高度評價，可以帶來新的工作機會和更高的薪水。此外，它為個人提供了他們所需的技能和知識，以設計，構建和維護可靠，可擴展和安全的數據處理系統。

>> Professional-Data-Engineer資料 <<

實用的Professional-Data-Engineer資料 |高通過率的考試材料|有效的Professional-Data-Engineer：Google Certified Professional Data Engineer Exam

對于KaoGuTi最近更新的Google Professional-Data-Engineer考古題，我們知道，只有有效和最新的Professional-Data-Engineer題庫可以幫助大家通過考試，這是由眾多考生證明過的事實。請嘗試Google Professional-Data-Engineer考古題最新的PDF和APP版本的題庫，由專家認證并覆蓋考試各個方面，能充分有效的幫助您補充相關的Professional-Data-Engineer考試知識點。不放棄下一秒就是希望，趕緊抓住您的希望吧，選擇Professional-Data-Engineer考古題，助您順利通過考試！

最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q151-Q156):

問題 #151
Case Study: 2 - MJTelco
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

A. The disk size per worker
B. The number of workers
C. The maximum number of workers
D. The zone

答案：D

問題 #152
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

A. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
B. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
C. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
D. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull

答案：B

解題說明：
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

問題 #153
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Userstable consisting of a FirstNamefield and a LastNamefield. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullNamefield consisting of the value of the FirstNamefield concatenated with a space, followed by the value of the LastNamefield for each employee. How can you make that data available while minimizing cost?

A. Create a view in BigQuery that concatenates the FirstNameand LastNamefield values to produce the FullName.
B. Add a new column called FullNameto the Users table. Run an UPDATEstatement that updates the FullNamecolumn for each user with the concatenation of the FirstNameand LastNamevalues.
C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Userstable, concatenates the FirstNamevalue and LastNamevalue for each user, and loads the proper values for FirstName, LastName, and FullNameinto a new table in BigQuery.
D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastNameand FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

答案：C

解題說明：
Explanation/Reference:

問題 #154
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have different features from the mutated samples in the database.
B. There are roughly equal occurrences of both normal and mutated samples in the database.
C. There are very few occurrences of mutations relative to normal samples.
D. You expect future mutations to have similar features to the mutated samples in the database.
E. You already have labels for which samples are mutated and which are normal in the database.

答案：C,D

解題說明：
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection

問題 #155
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority
of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the
cost of queries, your organization created a view called events, which queries only the last 14 days of
data. The view is described in legacy SQL. Next month, existing applications will be connecting to
BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can
connect. Which two actions should you take? (Choose two.)

A. Create a new view over events using standard SQL
B. Create a new view over events_partitioned using standard SQL
C. Create a service account for the ODBC connection to use for authentication
D. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection
and shared "events"
E. Create a new partitioned table using a standard SQL query

答案：A,D

問題 #156
......

很多人都認為要通過一些高難度的Professional-Data-Engineer認證考試是需要精通很多Google專業知識。只有掌握很全面的IGoogle知識的人才會有資格去報名參加的考試。其實現在有很多方法可以幫你彌補你的知識不足的，一樣能通過Professional-Data-Engineer認證考試，也許比那些專業知識相當全面的人花的時間和精力更少，正所謂條條大路通羅馬。

新版Professional-Data-Engineer題庫上線: https://www.kaoguti.com/Professional-Data-Engineer_exam-pdf.html

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