如果你使用了我們的Google的Professional-Data-Engineer認證考試解析學習資料資源，一定會減少考試的時間成本和經濟成本，有助於你順利通過考試，在你決定購買我們Google的Professional-Data-Engineer認證考試解析之前，你可以下載我們的部門免費試題，其中有PDF版本和軟體版本，如果需要軟體版本請及時與我們客服人員索取。 許多考生花費了大量的時間和精力學習Google Professional-Data-Engineer認證考試解析考試相關知識，但是到最後卻沒有成功，分析他們失敗的原因，我們得出結論是沒有針對性的復習。現在，Shobhadoshi專門針對認證考試研發出有針對性的Google Professional-Data-Engineer認證考試解析考古題，為考生獲得認證節約更多的時間和金錢。 Google的Professional-Data-Engineer認證考試解析考試其實是一個技術專家考試， Google的Professional-Data-Engineer認證考試解析考試可以幫助和促進IT人員有一個優秀的IT職業生涯，有了好的職業生涯，當然你就可以為國家甚至企業創造源源不斷的利益，從而去促進國家經濟發展，如果所有的IT人員都這樣，那麼民富則國強。

Google Cloud Certified Professional-Data-Engineer 如果你有夢想就去捍衛它。

但是要通過Google Professional-Data-Engineer - Google Certified Professional Data Engineer Exam認證考試解析認證考試不是那麼簡單。 那麼，你也想拿到這個考試的認證資格嗎？那麼趕緊報名參加吧，Shobhadoshi可以幫助你，所以不用擔心。你的夢想是什麼？難道你不想在你的職業生涯中做出一番閃耀的成就嗎？肯定是想的吧。

Shobhadoshi就是一個專門為IT專業人士提供相關認證考試的資訊來源的網站。通過很多使用過Shobhadoshi的產品的人反映，Shobhadoshi被證明是最好的資訊來源網站。Shobhadoshi的產品是一個很可靠的培訓工具。

Google Professional-Data-Engineer認證考試解析 - 如果你還是不相信的話，那就趕快自己來體驗一下吧。

有了目標就要勇敢的去實現。每一個選擇IT行業的人應該都不會只是安於現狀那樣簡單點的生活，現在各行各業的競爭壓力可想而知，IT行業也不例外，所以你們要是有了目標就要勇敢的去實現，其中通過 Google的Professional-Data-Engineer認證考試解析考試認證也是一次不小的競爭方式之一，通過了此考試，那麼你的IT生涯將會大展宏圖，會有一幅不一樣的藍圖等著你去勾勒，而我們Shobhadoshi網站可以提供你真實準確的培訓資料，幫助你通過考試獲得認證，從而實現你的藍圖理想。

這個考古題為你搜集並解析了很多優秀的過去考試考過的問題，並且根據最新的大綱加入了很多可能出現的新問題。这是一个可以保证你一次通过考试的考古題。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?
A. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.
B. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to
GCS.
C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a
Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
D. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
Answer: B

QUESTION NO: 2
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud Dataflow
Answer: C
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive

QUESTION NO: 3
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
C. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C

QUESTION NO: 4
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
Answer: D
Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud
Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your
Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

QUESTION NO: 5
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C

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Updated: May 27, 2022