Google Professional-Data-Engineer試験問題認定試験の難しさで近年にほとんどの受験生は資格認定試験に合格しなっかたと良く知られます。だから、我々社の有効な試験問題集は長年にわたりGoogle Professional-Data-Engineer試験問題認定資格試験問題集作成に取り組んだIT専門家によって書いてます。実際の試験に表示される質問と正確な解答はあなたのGoogle Professional-Data-Engineer試験問題認定資格試験合格を手伝ってあげます。 周りの多くの人は全部Google Professional-Data-Engineer試験問題資格認定試験にパースしまして、彼らはどのようにできましたか。今には、あなたにOmgzlookを教えさせていただけませんか。 後、弊社のProfessional-Data-Engineer試験問題資料はすべてProfessional-Data-Engineer試験問題試験に関わることがわかります。
Google Cloud Certified Professional-Data-Engineer 最もよくて最新で資料を提供いたします。
GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam試験問題の認定試験に合格するのは簡単ではなくて、OmgzlookはProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam試験問題試験の受験生がストレスを軽減し、エネルギーと時間を節約するために専門研究手段として多様な訓練を開発して、Omgzlookから君に合ったツールを選択してください。 Google Professional-Data-Engineer 参考書「Google Certified Professional Data Engineer Exam」認証試験に合格することが簡単ではなくて、Google Professional-Data-Engineer 参考書証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。
現在の社会の中で優秀な人材が揃てIT人材も多く、競争もとても大きくて、だから多くのIT者はにIT関する試験に参加するIT業界での地位のために奮闘しています。Professional-Data-Engineer試験問題試験はGoogleの一つ重要な認証試験で多くのIT専門スタッフが認証される重要な試験です。
Google Professional-Data-Engineer試験問題 - 成功を祈ります。
Omgzlookは実際の環境で本格的なGoogleのProfessional-Data-Engineer試験問題「Google Certified Professional Data Engineer Exam」の試験の準備過程を提供しています。もしあなたは初心者若しくは専門的な技能を高めたかったら、OmgzlookのGoogleのProfessional-Data-Engineer試験問題「Google Certified Professional Data Engineer Exam」の試験問題があなたが一歩一歩自分の念願に近くために助けを差し上げます。試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。しかも、一年間の無料更新サービスを提供します。
GoogleのProfessional-Data-Engineer試験問題認定試験「Google Certified Professional Data Engineer Exam」によい準備ができて、試験に穏やかな心情をもって扱うことができます。Omgzlookの専門家が研究された問題集を利用してください。
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