Googleの認証資格は最近ますます人気になっていますね。国際的に認可された資格として、Googleの認定試験を受ける人も多くなっています。その中で、Professional-Data-Engineer学習関連題認定試験は最も重要な一つです。 Professional-Data-Engineer学習関連題問題集の合格率は高いです。また、弊社はいいサービスを提供します。 Omgzlookを選んだら、あなたは簡単に認定試験に合格することができますし、あなたはITエリートたちの一人になることもできます。
Google Cloud Certified Professional-Data-Engineer 私の夢は最高のIT専門家になることです。
Google Cloud Certified Professional-Data-Engineer学習関連題 - Google Certified Professional Data Engineer Exam 私たちはお客様のための利益を求めるのを追求します。 きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。
しかし、GoogleのProfessional-Data-Engineer学習関連題認定試験に合格するという夢は、Omgzlookに対して、絶対に掴められます。Omgzlookは親切なサービスで、GoogleのProfessional-Data-Engineer学習関連題問題集が質の良くて、GoogleのProfessional-Data-Engineer学習関連題認定試験に合格する率も１００パッセントになっています。Omgzlookを選ぶなら、私たちは君の認定試験に合格するのを保証します。
Google Professional-Data-Engineer学習関連題 - 弊社の開発したソフトは非常に全面的です。
OmgzlookのGoogleのProfessional-Data-Engineer学習関連題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学習関連題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学習関連題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。
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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