Shobhadoshi는 자격증 응시자에게Google Professional-Data-Engineer최신버전자료 시험 준비를 위한 현재 그리고 가장 최근의 자료들을 제공하는 이 산업 영역의 리더입니다. Shobhadoshi는Google Professional-Data-Engineer최신버전자료덤프를 시험문제변경에 따라 계속 갱신하여 고객님께서 받은 것이Google Professional-Data-Engineer최신버전자료 시험의 가장 최신 기출문제임을 보증해드립니다. Shobhadoshi에서는 여러분들의 시간돠 돈을 절약해드리기 위하여 저렴한 가격에 최고의 품질을 지닌 퍼펙트한Google인증Professional-Data-Engineer최신버전자료시험덤플르 제공해드려 고객님의 시험준비에 편안함을 선물해드립니다. Shobhadoshi제품을 한번 믿어보세요. 또한 구매 후 일년무료 업데이트버전을 받을 수 있는 기회를 얻을 수 있습니다.

Google인증 Professional-Data-Engineer최신버전자료시험은 IT인증시험중 가장 인기있는 시험입니다.

Shobhadoshi에서 제공하는 덤프로 여러분은 1000%시험을 패스하실수 있고Google Professional-Data-Engineer - Google Certified Professional Data Engineer Exam최신버전자료자격증을 취득하실 수 있습니다.지금 바로 사이트에서Google Professional-Data-Engineer - Google Certified Professional Data Engineer Exam최신버전자료덤프데모 즉 덤프의 일부 문제와 답을 다운 받으셔서 체험하실 수 있습니다. Google인증 Professional-Data-Engineer 인증자료시험이 어려워 자격증 취득을 망설이는 분들이 많습니다. Shobhadoshi가 있으면 이런 걱정은 하지 않으셔도 됩니다.

여러분이 다른 사이트에서도Google인증Professional-Data-Engineer최신버전자료시험 관련덤프자료를 보셨을 것입니다 하지만 우리Shobhadoshi의 자료만의 최고의 전문가들이 만들어낸 제일 전면적이고 또 최신 업데이트일 것입니다.우리덤프의 문제와 답으로 여러분은 꼭 한번에Google인증Professional-Data-Engineer최신버전자료시험을 패스하실 수 있습니다.

만약Google Google Professional-Data-Engineer최신버전자료자격증이 있으시다면 여러분은 당연히 경쟁력향상입니다.

자신을 부단히 업그레이드하려면 많은 노력이 필요합니다. IT업종 종사자라면 국제승인 IT인증자격증을 취득하는것이 자신을 업그레이드하는것과 같습니다. Google인증 Professional-Data-Engineer최신버전자료시험을 패스하여 원하는 자격증을 취득하려면Shobhadoshi의Google인증 Professional-Data-Engineer최신버전자료덤프를 추천해드립니다. 하루빨리 덤프를 공부하여 자격증 부자가 되세요.

시험공부할 시간이 충족하지 않은 분들은Shobhadoshi 에서 제공해드리는Google 인증Professional-Data-Engineer최신버전자료덤프로 시험준비를 하시면 자격증 취득이 쉬워집니다. 덤프를 구매하시면 일년무료 업데이트서비스도 받을수 있습니다.

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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: 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 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: 5
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

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Updated: May 27, 2022