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지금 같은 정보시대에, 많은 IT업체 등 사이트에Google Professional-Data-Engineer공부자료인증관련 자료들이 제공되고 있습니다, 하지만 이런 사이트들도 정확하고 최신 시험자료 확보는 아주 어렵습니다. 그들의Google Professional-Data-Engineer공부자료자료들은 아주 기본적인 것들뿐입니다. 전면적이지 못하여 응시자들의 관심을 쌓지 못합니다. Royalholidayclubbed덤프는 선택하시면 성공을 선택한것입니다. Google인증 Professional-Data-Engineer공부자료 시험은 최근 제일 인기있는 인증시험입니다. 그중에서 대부분 분들이Royalholidayclubbed제품에 많은 관심과 사랑을 주고 계시는데 그 원인은 무엇일가요?바로Royalholidayclubbed에서 제공해드리는 덤프자료 품질이 제일 좋고 업데이트가 제일 빠르고 가격이 제일 저렴하고 구매후 서비스가 제일 훌륭하다는 점에 있습니다.

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Royalholidayclubbed의 Google인증 Professional-Data-Engineer - Google Certified Professional Data Engineer Exam공부자료덤프를 구매하여 공부한지 일주일만에 바로 시험을 보았는데 고득점으로 시험을 패스했습니다.이는Royalholidayclubbed의 Google인증 Professional-Data-Engineer - Google Certified Professional Data Engineer Exam공부자료덤프를 구매한 분이 전해온 희소식입니다. Google인증 Professional-Data-Engineer 최신시험후기시험패스를 원하신다면Royalholidayclubbed의 제품이 고객님의 소원을 들어줄것입니다. Google인증 Professional-Data-Engineer 최신시험후기시험패스 공부방법을 찾고 있다면 제일 먼저Royalholidayclubbed를 추천해드리고 싶습니다.

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Google인증 Professional-Data-Engineer공부자료시험이 너무 어려워 보여서 오르지못할 산처럼 보이시나요? 그건Royalholidayclubbed의 Google인증 Professional-Data-Engineer공부자료시험문제에 대비하여 제작한Google인증 Professional-Data-Engineer공부자료덤프가 있다는 것을 모르고 있기때문입니다. Google인증 Professional-Data-Engineer공부자료시험에 도전하고 싶으시다면 최강 시험패스율로 유명한Royalholidayclubbed의 Google인증 Professional-Data-Engineer공부자료덤프로 시험공부를 해보세요.시간절약은 물론이고 가격도 착해서 간단한 시험패스에 딱 좋은 선택입니다.

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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

 

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