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Google Cloud Certified Professional-Data-Engineer でも、成功へのショートカットがを見つけました。
我々の開発するGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam試験対策書ソフトは最新で最も豊富な問題集を含めています。 IT業種で仕事しているあなたは、夢を達成するためにどんな方法を利用するつもりですか。実際には、IT認定試験を受験して認証資格を取るのは一つの良い方法です。
我々OmgzlookのITエリートと我々のGoogleのProfessional-Data-Engineer試験対策書試験のソフトに満足するお客様は我々に自信を持たせます。あなたのGoogleのProfessional-Data-Engineer試験対策書試験を準備する圧力を減少するのは我々の責任で、あなたにGoogleのProfessional-Data-Engineer試験対策書試験に合格させるのは我々の目標です。我々はほぼ100％の通過率であなたに安心させます。
Google Professional-Data-Engineer試験対策書 - Omgzlookから大変助かりました。
OmgzlookのGoogleのProfessional-Data-Engineer試験対策書試験トレーニング資料は豊富な経験を持っているIT専門家が研究したものです。君がGoogleのProfessional-Data-Engineer試験対策書問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もしGoogleのProfessional-Data-Engineer試験対策書問題集は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。
70％の問題は解説がありますし、試験の内容を理解しやすいと助けます。常にGoogle Professional-Data-Engineer試験対策書試験に参加する予定があるお客様は「こちらの問題集には、全部で何問位、掲載されておりますか？」といった質問を提出しました。
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