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Google Cloud Certified Professional-Data-Engineer 不思議でしょう。Google Cloud Certified Professional-Data-Engineer的中率 - Google Certified Professional Data Engineer Exam 試験を申し込みたいあなたは、いまどうやって試験に準備すべきなのかで悩んでいますか。 Royalholidayclubbedは専門的で、たくさんの受験生のために、君だけのために存在するのです。それは正確的な試験の内容を保証しますし、良いサービスで、安い価格で営業します。
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Google Professional-Data-Engineer的中率 - 我々の誠意を信じてください。あなたへの紹介よりあなたに自分で体験させたほうがいいと思います。弊社のRoyalholidayclubbedで無料でGoogleのProfessional-Data-Engineer的中率ソフトのデモを直ちにダウンロードできます。GoogleのProfessional-Data-Engineer的中率ソフトを利用してこのソフトはあなたの愛用するものになることを信じています。GoogleのProfessional-Data-Engineer的中率ソフトはあなたにITという職業での人材に鳴らせます。
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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 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: 4 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
QUESTION NO: 5 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
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Updated: May 27, 2022
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