Professional-Data-Engineer試験過去問 & Google Certified Professional-Data-Engineer Exam復習対策 - Royalholidayclubbed

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それに、Royalholidayclubbedの教材を購入すれば、Royalholidayclubbedは一年間の無料アップデート・サービスを提供してあげます。問題が更新される限り、Royalholidayclubbedは直ちに最新版のProfessional-Data-Engineer試験過去問資料を送ってあげます。そうすると、あなたがいつでも最新バージョンの資料を持っていることが保証されます。 GoogleのProfessional-Data-Engineer試験過去問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Royalholidayclubbed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。 GoogleのProfessional-Data-Engineer試験過去問認定試験は現在のIT領域で本当に人気がある試験です。

それはRoyalholidayclubbedのProfessional-Data-Engineer試験過去問問題集です。

Google Cloud Certified Professional-Data-Engineer試験過去問 - Google Certified Professional Data Engineer Exam あなたより優れる人は存在している理由は彼らはあなたの遊び時間を効率的に使用できることです。ここには、私たちは君の需要に応じます。RoyalholidayclubbedのGoogleのProfessional-Data-Engineer 関連受験参考書問題集を購入したら、私たちは君のために、一年間無料で更新サービスを提供することができます。

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Google Professional-Data-Engineer試験過去問 - Royalholidayclubbedは君のもっともよい選択ですよ。

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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

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Updated: May 27, 2022

by RHC.

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