GoogleのProfessional-Data-Engineer最新知識ソフトはあなたにITという職業での人材に鳴らせます。あなたへの紹介よりあなたに自分で体験させたほうがいいと思います。弊社のGoldmile-Infobizで無料でGoogleのProfessional-Data-Engineer最新知識ソフトのデモを直ちにダウンロードできます。 数年間の発展で我々Goldmile-Infobizはもっと多くの資源と経験を得ています。改善されているソフトはあなたのGoogleのProfessional-Data-Engineer最新知識試験の復習の効率を高めることができます。 我々Goldmile-Infobizがあなたに提供するGoogleのProfessional-Data-Engineer最新知識ソフトはこれを保証します。
Google Cloud Certified Professional-Data-Engineer 」と感謝します。
弊社の信頼できるProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam最新知識問題集を使用したお客様はほとんど試験に合格しました。 有効的なGoogle Professional-Data-Engineer 予想試験認定資格試験問題集を見つけられるのは資格試験にとって重要なのです。我々Goldmile-InfobizのGoogle Professional-Data-Engineer 予想試験試験問題と試験解答の正確さは、あなたの試験準備をより簡単にし、あなたが試験に高いポイントを得ることを保証します。
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Google Professional-Data-Engineer最新知識 - 本当に助かりました。
数年間でのIT認定試験資料向けの研究分析によって、我々社はこの業界のリーダーにだんだんなっています。弊社のチームは開発される問題集はとても全面で、受験生をGoogle Professional-Data-Engineer最新知識試験に合格するのを良く助けます。周知のように、Google Professional-Data-Engineer最新知識資格認定があれば、IT業界での発展はより簡単になります。
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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