世界は変化している、我々はできるだけそのペースを維持する必要があります。我々Goldmile-InfobizはGoogleのProfessional-Data-Engineer資格関連題試験の変化を注目しています。数年以来の試験問題集を研究しています。 Goldmile-Infobizのトレーニング資料は実践の検証に合格すたもので、多くの受験生に証明された１００パーセントの成功率を持っている資料です。Goldmile-Infobizを利用したら、あなたは自分の目標を達成することができ、最良の結果を得ます。 弊社のGoogleのProfessional-Data-Engineer資格関連題真題によって、資格認定証明書を受け取れて、仕事の昇進を実現できます。
Google Cloud Certified Professional-Data-Engineer しかも、サイトでテストデータの一部は無料です。
励ましだけであなたの試験への自信を高めるのは不可能だと知っていますから、我々は効果的なソフトを提供してあなたにGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam資格関連題試験に合格させます。 Goldmile-Infobiz で、あなたにあなたの宝庫を見つけられます。Goldmile-Infobiz はGoogleのProfessional-Data-Engineer 受験料試験に関連する知識が全部含まれていますから、あなたにとって難しい問題を全て解決して差し上げます。
人の職業の発展は彼の能力によって進めます。権威的な国際的な証明書は能力に一番よい証明です。GoogleのProfessional-Data-Engineer資格関連題試験の認証はあなたの需要する証明です。
Google Professional-Data-Engineer資格関連題 - もちろんです。
GoogleのProfessional-Data-Engineer資格関連題資格認定証明書を持つ人は会社のリーダーからご格別のお引き立てを賜ったり、仕事の昇進をたやすくなったりしています。これなので、今から我々社Goldmile-InfobizのProfessional-Data-Engineer資格関連題試験に合格するのに努力していきます。弊社のGoogleのProfessional-Data-Engineer資格関連題真題によって、資格認定証明書を受け取れて、仕事の昇進を実現できます。
もっと大切なのは、あなたもより多くの仕事のスキルをマスターしたことを証明することができます。では、はやくGoogleのProfessional-Data-Engineer資格関連題認定試験を受験しましょう。
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
Amazon AWS-Developer-JP - 弊社は1年間の無料更新サービスを提供いたします。 Network Appliance NS0-528 - 信じられなら利用してみてください。 Apple DEP-2025 - 顧客様と販売者の間での信頼性は苦労かつ大切なことだと良く知られます。 CIPS L5M5 - Goldmile-Infobizの問題集は100％の合格率を持っています。 最もよい方法はAmazon DOP-C02-JPN問題集を買うことです。
Updated: May 27, 2022