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Google Cloud Certified Professional-Data-Engineer Goldmile-Infobizというサイトです。
Google Cloud Certified Professional-Data-Engineer試験解説 - Google Certified Professional Data Engineer Exam Goldmile-Infobizの学習教材はいろいろな狙いを含まれていますし、カバー率が高いですから、初心者にしても簡単に身に付けられます。 Goldmile-InfobizのGoogleのProfessional-Data-Engineer 試験勉強過去問試験トレーニング資料は必要とするすべての人に成功をもたらすことができます。GoogleのProfessional-Data-Engineer 試験勉強過去問試験は挑戦がある認定試験です。
激変なネット情報時代で、質の良いGoogleのProfessional-Data-Engineer試験解説問題集を見つけるために、あなたは悩むことがありませんか。私たちは君がGoldmile-Infobizを選ぶことと正確性の高いGoogleのProfessional-Data-Engineer試験解説問題集を祝っています。Goldmile-InfobizのGoogleのProfessional-Data-Engineer試験解説問題集が君の認定試験に合格するのに大変役に立ちます。
Google Professional-Data-Engineer試験解説 - もちろんです。
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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
例えば、我々のよく発売されているGoogleのMicrosoft DP-700試験ソフトは大量の試験問題への研究によって作れることです。 Microsoft PL-300-KR - 信じられなら利用してみてください。 Cisco 300-535 - 我々は、失敗の言い訳ではなく、成功する方法を見つけます。 そのPECB ISO-9001-Lead-Auditor参考資料はIT認定試験の準備に使用することができるだけでなく、自分のスキルを向上させるためのツールとして使えることもできます。 だから、我々の専門家たちはタイムリーにGoogleのPECB ISO-9001-Lead-Auditor資料を更新していて、我々の商品を利用している受験生にGoogleのPECB ISO-9001-Lead-Auditor試験の変革とともに進めさせます。
Updated: May 27, 2022