神様は私を実力を持っている人間にして、美しい人形ではないです。IT業種を選んだ私は自分の実力を証明したのです。しかし、神様はずっと私を向上させることを要求します。 Goldmile-InfobizのGoogleのProfessional-Data-Engineer資格トレーリング試験トレーニング資料はIT認証試験を受ける全ての受験生が試験に合格することを助けるもので、受験生からの良い評価をたくさんもらいました。Goldmile-Infobizを選ぶのは成功を選ぶのに等しいです。 ここにはあなたが最も欲しいものがありますから。
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Professional-Data-Engineer - Google Certified Professional Data Engineer Exam資格トレーリング問題集は全面的かつわかりやすいです。 多くの人々はGoogleのProfessional-Data-Engineer 受験練習参考書試験に合格できるのは難しいことであると思っています。この悩みに対して、我々社Goldmile-InfobizはGoogleのProfessional-Data-Engineer 受験練習参考書試験に準備するあなたに専門的なヘルプを与えられます。
Professional-Data-Engineer資格トレーリング試験資料の3つのバージョンのなかで、PDFバージョンのProfessional-Data-Engineer資格トレーリングトレーニングガイドは、ダウンロードと印刷でき、受験者のために特に用意されています。携帯電話にブラウザをインストールでき、 私たちのProfessional-Data-Engineer資格トレーリング試験資料のApp版を使用することもできます。 PC版は、実際の試験環境を模擬し、Windowsシステムのコンピュータに適します。
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近年、IT業種の発展はますます速くなることにつれて、ITを勉強する人は急激に多くなりました。人々は自分が将来何か成績を作るようにずっと努力しています。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 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