OmgzlookのGoogleのProfessional-Data-Engineer模擬体験試験トレーニング資料は豊富な経験を持っているIT専門家が研究したものです。君がGoogleのProfessional-Data-Engineer模擬体験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GoogleのProfessional-Data-Engineer模擬体験問題集は問題があれば、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。 ターゲットがなくてあちこち参考資料を探すのをやめてください。どんな資料を利用すべきなのかがわからないとしたら、OmgzlookのProfessional-Data-Engineer模擬体験問題集を利用してみましょう。 これも弊社が自信的にあなたに商品を薦める原因です。
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OmgzlookのGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam模擬体験試験トレーニング資料は豊富な経験を持っている専門家が長年の研究を通じて開発されたものです。 我々の提供するPDF版のGoogleのProfessional-Data-Engineer 認証Pdf資料試験の資料はあなたにいつでもどこでも読めさせます。我々もオンライン版とソフト版を提供します。
そうであれば、無料で弊社の提供するGoogleのProfessional-Data-Engineer模擬体験のデモをダウンロードしてみよう。我々提供する資料はあなたの需要だと知られています。あなたのGoogleのProfessional-Data-Engineer模擬体験試験に参加する圧力を減ってあなたの効率を高めるのは我々の使命だと思います。
Google Professional-Data-Engineer模擬体験 - できるだけ100％の通過率を保証使用にしています。
数年以来の整理と分析によって開発されたProfessional-Data-Engineer模擬体験問題集は権威的で全面的です。Professional-Data-Engineer模擬体験問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からProfessional-Data-Engineer模擬体験問題集への好評をもらいました。Professional-Data-Engineer模擬体験問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するProfessional-Data-Engineer模擬体験問題集を暗記すれば、きっと試験に合格できます。
GoogleのProfessional-Data-Engineer模擬体験試験のソフトは問題数が豊富であなたに大量の練習で能力を高めさせます。そのほかに、専門家たちの解答への詳しい分析があります。
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