弊社のProfessional-Data-Engineer専門試験問題集は大勢の専門家たちの努力で開発される成果です。初心者といい、数年IT仕事を従事した人といい、我々Goldmile-InfobizのGoogle Professional-Data-Engineer専門試験問題集は最良の選択であると考えられます。なぜならば、弊社は高品質かつ改革によってすぐに更新できるProfessional-Data-Engineer専門試験問題集を提供できるからです。 Goldmile-Infobizが提供したGoogleのProfessional-Data-Engineer専門試験トレーニング資料を利用する方法です。あなたが試験に合格することにヘルプをあげられますから。 短い時間でProfessional-Data-Engineer専門試験資格認定を取得するような高いハイリターンは嬉しいことではないでしょうか。
Google Cloud Certified Professional-Data-Engineer もし失敗だったら、我々は全額で返金します。
あなたの取得したGoogle Professional-Data-Engineer - Google Certified Professional Data Engineer Exam専門試験資格認定は、仕事中に核心技術知識を同僚に認可されるし、あなたの技術信頼度を増強できます。 人生には様々な選択があります。選択は必ずしも絶対な幸福をもたらさないかもしれませんが、あなたに変化のチャンスを与えます。
Professional-Data-Engineer専門試験認定試験の問題集は大勢の人の注目を集め、とても人気がある商品です。Professional-Data-Engineer専門試験認定試験の問題集はなぜそんなに人気がありますか？Professional-Data-Engineer専門試験認定試験の問題集は最も全面的なIT知識を提供できるからです。では、躊躇しなくて、Google Professional-Data-Engineer専門試験認定試験の問題集を早く購入しましょう！
Google Professional-Data-Engineer専門試験 - 君の初めての合格を目標にします。
もし君の予算がちょっと不自由で、おまけに質の良いGoogleのProfessional-Data-Engineer専門試験試験トレーニング資料を購入したいなら、Goldmile-InfobizのGoogleのProfessional-Data-Engineer専門試験試験トレーニング資料を選択したほうが良いです。それは値段が安くて、正確性も高くて、わかりやすいです。いろいろな受験生に通用します。あなたはGoldmile-Infobizの学習教材を購入した後、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。
多くの人々は高い難度のIT認証試験に合格するのは専門の知識が必要だと思います。それは確かにそうですが、その知識を身につけることは難しくないとといわれています。
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
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: 3
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: 4
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: 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