Goldmile-Infobizを選られば、成功しましょう。わずか数年の中に、Google Professional-Data-Engineer関連復習問題集認定試験がたくさんの人の日常生活にとても大きい影響を与えています。簡単で順調にGoogle Professional-Data-Engineer関連復習問題集認定試験を通すのは問題になりますが、Goldmile-Infobizはこの問題を解決できるよ。 Goldmile-InfobizはIT試験問題集を提供するウエブダイトで、ここによく分かります。最もよくて最新で資料を提供いたします。 弊社の勉強の商品を選んで、多くの時間とエネルギーを節約こともできます。
Google Cloud Certified Professional-Data-Engineer それは受験者にとって重要な情報です。
Google Cloud Certified Professional-Data-Engineer関連復習問題集 - Google Certified Professional Data Engineer Exam 我々はあなたに向いて適当の資料を選びます。 Goldmile-InfobizはきみのIT夢に向かって力になりますよ。Goldmile-Infobizは多種なIT認証試験を受ける方を正確な資料を提供者でございます。
Goldmile-Infobiz で、あなたにあなたの宝庫を見つけられます。Goldmile-Infobiz はGoogleのProfessional-Data-Engineer関連復習問題集試験に関連する知識が全部含まれていますから、あなたにとって難しい問題を全て解決して差し上げます。Goldmile-InfobizのGoogleのProfessional-Data-Engineer関連復習問題集試験トレーニング資料は必要とするすべての人に成功をもたらすことができます。
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当面の実際のテストを一致させるために、Goldmile-InfobizのGoogleのProfessional-Data-Engineer関連復習問題集問題集の技術者はずべての変化によって常に問題と解答をアップデートしています。それに我々はいつもユーザーからのフィードバックを受け付け、アドバイスの一部をフルに活用していますから、完璧なGoldmile-InfobizのGoogleのProfessional-Data-Engineer関連復習問題集問題集を取得しました。Goldmile-Infobizはそれを通じていつまでも最高の品質を持っています。
自分のレベルを高めたいですか。では、仕事に役に立つスキルをもっと身に付けましょう。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 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
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
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
A. A Dataproc cluster cannot have only preemptible workers.
B. Preemptible workers cannot store data.
C. Preemptible workers cannot use persistent disk.
D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
Answer: A,B
Explanation
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms
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Updated: May 27, 2022