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その中で、Professional-Data-Engineer受験トレーリング認定試験は最も重要な一つです。

Google Cloud Certified Professional-Data-Engineer受験トレーリング - Google Certified Professional Data Engineer Exam ご安心で試験のために勉強します。 早速買いに行きましょう。RoyalholidayclubbedのGoogleのProfessional-Data-Engineer 資料的中率試験トレーニング資料を使ったら、君のGoogleのProfessional-Data-Engineer 資料的中率認定試験に合格するという夢が叶えます。

多くの人は結果が大丈夫で過程だけ重要ですって言いますが。GoogleのProfessional-Data-Engineer受験トレーリング試験にとってはそうではない。GoogleのProfessional-Data-Engineer受験トレーリング試験に合格するのはIT業界で働いているあなたに利益をもらわせることができます。

Google Professional-Data-Engineer受験トレーリング - それは正確性が高くて、カバー率も広いです。

IT業界で働いているあなたにとってのGoogleのProfessional-Data-Engineer受験トレーリング試験の重要性を知っていますから、我々はあなたを助けられるGoogleのProfessional-Data-Engineer受験トレーリングソフトを開発しました。我々はあなたにすべての資料を探して科学的に分析しました。これらをするのはあなたのGoogleのProfessional-Data-Engineer受験トレーリング試験を準備する圧力を減少するためです。

もちろん、我々はあなたに一番安心させるのは我々の開発する多くの受験生に合格させるGoogleのProfessional-Data-Engineer受験トレーリング試験のソフトウェアです。我々はあなたに提供するのは最新で一番全面的なGoogleのProfessional-Data-Engineer受験トレーリング問題集で、最も安全な購入保障で、最もタイムリーなGoogleのProfessional-Data-Engineer受験トレーリング試験のソフトウェアの更新です。

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

 

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