我々のソフトは多くの受験生にGoogleのProfessional-Data-Engineerテキスト試験に合格させました。我々の通過率はいくつ高くても、我々はあなたが試験に失敗したら全額で返金するのを保証します。これはあなたに安心で弊社の商品を購入させるためです。 こうして、君は安心で試験の準備を行ってください。弊社の資料を使って、100%に合格を保証いたします。 我々のデモを無料でやってみよう。
Google Cloud Certified Professional-Data-Engineer 逆境は人をテストすることができます。
Google Cloud Certified Professional-Data-Engineerテキスト - Google Certified Professional Data Engineer Exam もし弊社を選ばれば、100%の合格率を保証でございます。 この試験はあなたが自分の念願を達成するのを助けることができます。試験に合格する自信を持たなくても大丈夫です。
Goldmile-Infobiz提供した商品の品質はとても良くて、しかも更新のスピードももっともはやくて、もし君はGoogleのProfessional-Data-Engineerテキストの認証試験に関する学習資料をしっかり勉強して、成功することも簡単になります。
Google Professional-Data-Engineerテキスト - それでは、どのようにすればそれを達成できますか。
Goldmile-Infobizの問題集はIT専門家がGoogleのProfessional-Data-Engineerテキスト「Google Certified Professional Data Engineer Exam」認証試験について自分の知識と経験を利用して研究したものでございます。Goldmile-Infobizの問題集は真実試験の問題にとても似ていて、弊社のチームは自分の商品が自信を持っています。Goldmile-Infobizが提供した商品をご利用してください。もし失敗したら、全額で返金を保証いたします。
もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、Goldmile-Infobizは無料でサンプルを提供することができます。なぜ受験生のほとんどはGoldmile-Infobizを選んだのですか。
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