Goldmile-InfobizはGoogleのProfessional-Data-Engineer勉強の資料「Google Certified Professional Data Engineer Exam」試験に関する完全な資料を唯一のサービスを提供するサイトでございます。Goldmile-Infobizが提供した問題集を利用してGoogleのProfessional-Data-Engineer勉強の資料試験は全然問題にならなくて、高い点数で合格できます。Google Professional-Data-Engineer勉強の資料試験の合格のために、Goldmile-Infobizを選択してください。 Goldmile-Infobizは広い研究と実際を基づいている経験及び正確的な学習教材を提供できます。私たちは君の最も早い時間でGoogleのProfessional-Data-Engineer勉強の資料試験に合格するように頑張ります。 弊社の商品は試験の範囲を広くカバーすることが他のサイトがなかなか及ばならないです。
Google Cloud Certified Professional-Data-Engineer 成功の楽園にどうやって行きますか。
受験生のあなたを助けて時間とお金を節約したり、Professional-Data-Engineer - Google Certified Professional Data Engineer Exam勉強の資料試験に速く合格すると保証します。 Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。真実かつ信頼性の高いものだからこそ、Goldmile-Infobizの試験参考書は長い時間にわたってますます人気があるようになっています。
一般的には、IT技術会社ではGoogle Professional-Data-Engineer勉強の資料資格認定を持つ職員の給料は持たない職員の給料に比べ、15％より高いです。これなので、IT技術職員としてのあなたはGoldmile-InfobizのGoogle Professional-Data-Engineer勉強の資料問題集デモを参考し、試験の準備に速く行動しましょう。我々社はあなたがGoogle Professional-Data-Engineer勉強の資料試験に一発的に合格するために、最新版の備考資料を提供します。
Google Professional-Data-Engineer勉強の資料 - ローマは一日に建てられませんでした。
Goldmile-InfobizのGoogleのProfessional-Data-Engineer勉強の資料試験トレーニング資料は正確性が高くて、カバー率も広い。あなたがGoogleのProfessional-Data-Engineer勉強の資料認定試験に合格するのに最も良くて、最も必要な学習教材です。うちのGoogleのProfessional-Data-Engineer勉強の資料問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もし学習教材は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。
私たちのProfessional-Data-Engineer勉強の資料試験参考書は、あなたがProfessional-Data-Engineer勉強の資料試験に合格する前に最高のサービスを提供することを保証します。これは確かに大きなチャンスです。
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 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