GoogleのProfessional-Data-Engineer日本語版受験参考書は専門知識と情報技術の検査として認証試験で、Goldmile-Infobizはあなたに一日早くGoogleの認証試験に合格させて、多くの人が大量の時間とエネルギーを費やしても無駄になりました。Goldmile-Infobizにその問題が心配でなく、わずか20時間と少ないお金をを使って楽に試験に合格することができます。Goldmile-Infobizは君に対して特別の訓練を提供しています。 すなわちGoldmile-InfobizのProfessional-Data-Engineer日本語版受験参考書問題集を利用して試験の準備をすることです。Professional-Data-Engineer日本語版受験参考書認定試験を受験したいですか。 Goldmile-Infobizの問題集はIT専門家がGoogleのProfessional-Data-Engineer日本語版受験参考書「Google Certified Professional Data Engineer Exam」認証試験について自分の知識と経験を利用して研究したものでございます。
Google Cloud Certified Professional-Data-Engineer そうであれば、あなたは夢がある人だと思います。
Google Cloud Certified Professional-Data-Engineer日本語版受験参考書 - Google Certified Professional Data Engineer Exam ここにはあなたが最も欲しいものがありますから。 我々Goldmile-InfobizのITエリートと我々のGoogleのProfessional-Data-Engineer 最新受験攻略試験のソフトに満足するお客様は我々に自信を持たせます。あなたのGoogleのProfessional-Data-Engineer 最新受験攻略試験を準備する圧力を減少するのは我々の責任で、あなたにGoogleのProfessional-Data-Engineer 最新受験攻略試験に合格させるのは我々の目標です。
そうすれば、実際のProfessional-Data-Engineer日本語版受験参考書試験を受けるときに緊張をすることはないです。ですから、心のリラックスした状態で試験に出る問題を対応することができ、あなたの正常なレベルをプレイすることもできます。Goldmile-InfobizのProfessional-Data-Engineer日本語版受験参考書問題集はあなたを楽に試験の準備をやらせます。
そして、Google Professional-Data-Engineer日本語版受験参考書問題集は安くて、便利です。
IT業界の発展とともに、IT業界で働いている人への要求がますます高くなります。競争の中で排除されないように、あなたはGoogleのProfessional-Data-Engineer日本語版受験参考書試験に合格しなければなりません。たくさんの時間と精力で試験に合格できないという心配な心情があれば、我々Goldmile-Infobizにあなたを助けさせます。多くの受験生は我々のソフトでGoogleのProfessional-Data-Engineer日本語版受験参考書試験に合格したので、我々は自信を持って我々のソフトを利用してあなたはGoogleのProfessional-Data-Engineer日本語版受験参考書試験に合格する保障があります。
でも、Google Professional-Data-Engineer日本語版受験参考書復習教材を選ばれば、試験に合格することは簡単です。Professional-Data-Engineer日本語版受験参考書復習教材の内容は全面的で、価格は合理的です。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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
QUESTION NO: 2
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: 3
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
A. Recreate the table with a partitioning column and clustering column.
B. Create a separate table for each I
C. Use the LIMIT keyword to reduce the number of rows returned.
D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
Answer: C
QUESTION NO: 4
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
* The user profile: What the user likes and doesn't like to eat
* The user account information: Name, address, preferred meal times
* The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
A. BigQuery
B. Cloud Datastore
C. Cloud SQL
D. Cloud Bigtable
Answer: A
QUESTION NO: 5
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
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Updated: May 27, 2022