それに、ソフトを買ったあなたは一年間の無料更新サービスを得ています。ご安心で試験のために勉強します。GoogleのProfessional-Data-Engineer認定試験試験の準備に悩んでいますか。 君は他の人の一半の努力で、同じGoogleのProfessional-Data-Engineer認定試験認定試験を簡単に合格できます。Goldmile-Infobizはあなたと一緒に君のITの夢を叶えるために頑張ります。 もしあなたが試験に合格する決心があったら、我々のGoogleのProfessional-Data-Engineer認定試験ソフトを利用するのはあなたの試験に成功する有効な保障です。
Google Cloud Certified Professional-Data-Engineer PDF版、ソフト版、オンライン版があります。
何十ユーロだけでこのような頼もしいGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam認定試験試験の資料を得ることができます。 業界で有名なGoogle Professional-Data-Engineer 練習問題問題集販売会社として、購入意向があると、我々の商品を選んでくださいませんか。今の社会はますます激しく変化しているから、私たちはいつまでも危機意識を強化します。
今の社会はますます激しく変化しているから、私たちはいつまでも危機意識を強化します。キャンパース内のIT知識を学ぶ学生なり、IT職人なり、Professional-Data-Engineer認定試験試験資格認証証明書を取得して、社会需要に応じて自分の能力を高めます。我々社は最高のGoogle Professional-Data-Engineer認定試験試験問題集を開発し提供して、一番なさービスを与えて努力しています。
Google Professional-Data-Engineer認定試験 - 成功を祈ります。
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GoogleのProfessional-Data-Engineer認定試験認定試験「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。Goldmile-Infobiz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