Professional-Data-Engineer模擬資料、Google Professional-Data-Engineerオンライン試験 - Google Certified Professional-Data-Engineer Exam - Omgzlook

我々Omgzlookは一番行き届いたアフタサービスを提供します。Google Professional-Data-Engineer模擬資料試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにGoogle Professional-Data-Engineer模擬資料試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。 だから、我々社の有効な試験問題集は長年にわたりGoogle Professional-Data-Engineer模擬資料認定資格試験問題集作成に取り組んだIT専門家によって書いてます。実際の試験に表示される質問と正確な解答はあなたのGoogle Professional-Data-Engineer模擬資料認定資格試験合格を手伝ってあげます。 我々社サイトのGoogle Professional-Data-Engineer模擬資料問題庫は最新かつ最完備な勉強資料を有して、あなたに高品質のサービスを提供するのはProfessional-Data-Engineer模擬資料資格認定試験の成功にとって唯一の選択です。

Google Cloud Certified Professional-Data-Engineer 最もよくて最新で資料を提供いたします。

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現在の社会の中で優秀な人材が揃てIT人材も多く、競争もとても大きくて、だから多くのIT者はにIT関する試験に参加するIT業界での地位のために奮闘しています。Professional-Data-Engineer模擬資料試験はGoogleの一つ重要な認証試験で多くのIT専門スタッフが認証される重要な試験です。

Google Professional-Data-Engineer模擬資料 - 成功を祈ります。

Omgzlookは実際の環境で本格的なGoogleのProfessional-Data-Engineer模擬資料「Google Certified Professional Data Engineer Exam」の試験の準備過程を提供しています。もしあなたは初心者若しくは専門的な技能を高めたかったら、OmgzlookのGoogleのProfessional-Data-Engineer模擬資料「Google Certified Professional Data Engineer Exam」の試験問題があなたが一歩一歩自分の念願に近くために助けを差し上げます。試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。しかも、一年間の無料更新サービスを提供します。

GoogleのProfessional-Data-Engineer模擬資料認定試験「Google Certified Professional Data Engineer Exam」によい準備ができて、試験に穏やかな心情をもって扱うことができます。Omgzlookの専門家が研究された問題集を利用してください。

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