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Google Cloud Certified Professional-Data-Engineer 還會讓你又一個美好的前程。

選擇我們的Professional-Data-Engineer - Google Certified Professional Data Engineer Exam考試大綱題庫資料可以保證你可以在短時間內學習及加強IT專業方面的知識，所以信任Shobhadoshi是您最佳的選擇！ 我們Shobhadoshi網站是在盡最大的努力為廣大考生提供最好最便捷的服務。速度和高效率當然不可避免，在當今的社會裏，高效率走到哪里都是熱議的話題，所以我們網站為廣大考生設計了一個高效率的培訓資料，可以讓考生迅速領悟，從而考試取得優異的成績。

通過Google Professional-Data-Engineer考試大綱的考試是不簡單的，選擇合適的培訓是你成功的第一步，選擇好的資訊來源是你成功的保障，而Shobhadoshi的產品是有很好的資訊來源保障。如果你選擇了Shobhadoshi的產品不僅可以100%保證你通過Google Professional-Data-Engineer考試大綱認證考試，還可以為你提供長達一年的免費更新。

Google Professional-Data-Engineer考試大綱 - 這樣討得上司的喜歡，還不如用實力說話。

在現在這個人才濟濟的社會裏，還是有很多行業是缺乏人才的，比如IT行業就相當缺乏技術性的人才。而Google Professional-Data-Engineer考試大綱 認證考試就是個檢驗IT技術的認證考試之一。Shobhadoshi是一個給你培訓Google Professional-Data-Engineer考試大綱 認證考試相關技術知識的網站。

機會從來都是屬於那些有準備的人。但是，當屬於我們的機會到來的時候我們是否能成功地抓住它呢？正在準備Google的Professional-Data-Engineer考試大綱考試的你，是否抓住了Shobhadoshi這個可以讓你成功的機會呢？Shobhadoshi的Professional-Data-Engineer考試大綱資料是你可以順利通過考試的保障，有了它，你將節省大量的時間，高效率地準備考試。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning.
What should you do?
A. Build and train a text classification model using TensorFlow. Deploy the model using Cloud
Machine Learning Engine. Call the model from your application and process the results as labels.
B. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
C. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes
Engine cluster. Call the model from your application and process the results as labels.
D. Call the Cloud Natural Language API from your application. Process the generated Sentiment
Analysis as labels.
Answer: D

QUESTION NO: 2
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
A. 'bigquery-public-data.noaa_gsod.gsod*`
B. 'bigquery-public-data.noaa_gsod.gsod'*
C. 'bigquery-public-data.noaa_gsod.gsod'
D. bigquery-public-data.noaa_gsod.gsod*
Answer: A

QUESTION NO: 3
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C

QUESTION NO: 4
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D

QUESTION NO: 5
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query
BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Load the data every 30 minutes into a new partitioned table in BigQuery.
Answer: D

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Updated: May 27, 2022