自分に合っている優秀な参考資料がほしいとしたら、一番来るべき場所はGoldmile-Infobizです。Goldmile-Infobizの知名度が高くて、IT認定試験に関連するいろいろな優秀な問題集を持っています。それに、すべてのProfessional-Data-Engineerテスト問題集試験問題集に対する無料なdemoがあります。 GoogleのProfessional-Data-Engineerテスト問題集試験に関する権威のある学習教材を見つけないで、悩んでいますか？世界中での各地の人々はほとんどGoogleのProfessional-Data-Engineerテスト問題集試験を受験しています。GoogleのProfessional-Data-Engineerテスト問題集の認証試験の高品質の資料を提供しているユニークなサイトはGoldmile-Infobizです。 まだ何を待っていますか。
Google Cloud Certified Professional-Data-Engineer 自分自身のIT技能を増強したいか。
例えば、我々のよく発売されているGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Examテスト問題集試験ソフトは大量の試験問題への研究によって作れることです。 一年間のソフト無料更新も失敗して全額での返金も我々の誠のアフターサービスでございます。弊社のGoldmile-InfobizはGoogleのProfessional-Data-Engineer 試験時間試験を準備している人々に保障を提供しています。
IT業界の発展とともに、IT業界で働いている人への要求がますます高くなります。競争の中で排除されないように、あなたはGoogleのProfessional-Data-Engineerテスト問題集試験に合格しなければなりません。たくさんの時間と精力で試験に合格できないという心配な心情があれば、我々Goldmile-Infobizにあなたを助けさせます。
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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
この悩みに対して、我々社Goldmile-InfobizはGoogleのCisco 300-425試験に準備するあなたに専門的なヘルプを与えられます。 IBM C1000-201試験のために、気楽に準備したり、参加したりしています。 Microsoft PL-200 - 勉強中で、何の質問があると、メールで我々はあなたのためにすぐ解決します。 ServiceNow CSA - 我が社のサービスもいいです。 Microsoft PL-300-KR練習資料が最も全面的な参考書です。
Updated: May 27, 2022