我々のGoogleのProfessional-Data-Engineer関連資格試験対応ソフトはあなたのすべての需要を満たすのを希望します。問題集の全面性と権威性、GoogleのProfessional-Data-Engineer関連資格試験対応ソフトがPDF版、オンライン版とソフト版があるという資料のバーションの多様性、購入の前にデモの無料ダウンロード、購入の後でGoogleのProfessional-Data-Engineer関連資格試験対応ソフトの一年間の無料更新、これ全部は我々の誠の心を示しています。 Goldmile-Infobizは１００％の合格率を保証するだけでなく、1年間の無料なオンラインの更新を提供しております。最新の資源と最新の動態が第一時間にお客様に知らせいたします。 我々の商品にあなたを助けさせましょう。
Google Cloud Certified Professional-Data-Engineer 人生には様々な選択があります。
Professional-Data-Engineer - Google Certified Professional Data Engineer Exam関連資格試験対応認定試験の問題集は大勢の人の注目を集め、とても人気がある商品です。 このような保証があれば、Goldmile-InfobizのProfessional-Data-Engineer 受験料問題集を購入しようか購入するまいかと躊躇する必要は全くないです。この問題集をミスすればあなたの大きな損失ですよ。
だから、私たちは信頼されるに値します。Professional-Data-Engineer関連資格試験対応試験に合格するには、関連する教材を探す必要があります。しかし、Googleのウエブサイトを見ると、すぐいいProfessional-Data-Engineer関連資格試験対応教材を手に入れることができます。
Google Professional-Data-Engineer関連資格試験対応 - Goldmile-Infobizには専門的なエリート団体があります。
GoogleのProfessional-Data-Engineer関連資格試験対応試験に合格することは容易なことではなくて、良い訓練ツールは成功の保証でGoldmile-Infobizは君の試験の問題を準備してしまいました。君の初めての合格を目標にします。
もし君の予算がちょっと不自由で、おまけに質の良いGoogleのProfessional-Data-Engineer関連資格試験対応試験トレーニング資料を購入したいなら、Goldmile-InfobizのGoogleのProfessional-Data-Engineer関連資格試験対応試験トレーニング資料を選択したほうが良いです。それは値段が安くて、正確性も高くて、わかりやすいです。
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