Shobhadoshiは優れたIT情報のソースを提供するサイトです。Shobhadoshiで、あなたの試験のためのテクニックと勉強資料を見つけることができます。ShobhadoshiのGoogleのProfessional-Data-Engineer受験資格試験トレーニング資料は豊富な知識と経験を持っているIT専門家に研究された成果で、正確度がとても高いです。 では、躊躇しなくて、Google　Professional-Data-Engineer受験資格認定試験の問題集を早く購入しましょう！あなたはIT業界の玄人になりたいですか？ここでGoogle　Professional-Data-Engineer受験資格認定試験の問題集をお勧めます。 これは試験の準備をするために非常に効率的なツールですから。

Google Cloud Certified Professional-Data-Engineer Shobhadoshiは君にとってベストな選択になります。

Google Cloud Certified Professional-Data-Engineer受験資格 - Google Certified Professional Data Engineer Exam 君の初めての合格を目標にします。 うちのGoogleのProfessional-Data-Engineer 日本語版復習指南試験トレーニング資料を購入する前に、Shobhadoshiのサイトで、一部分のフリーな試験問題と解答をダンロードでき、試用してみます。君がうちの学習教材を購入した後、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。

多くの人々は高い難度のIT認証試験に合格するのは専門の知識が必要だと思います。それは確かにそうですが、その知識を身につけることは難しくないとといわれています。IT業界ではさらに強くなるために強い専門知識が必要です。

Google Professional-Data-Engineer受験資格 - それは確かに君の試験に役に立つとみられます。

世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、Professional-Data-Engineer受験資格問題集を学ぶ必要があります。弊社のProfessional-Data-Engineer受験資格問題集はあなたにこのチャンスを全面的に与えられます。あなたは自分の望ましいGoogle Professional-Data-Engineer受験資格問題集を選らんで、学びから更なる成長を求められます。心はもはや空しくなく、生活を美しくなります。

それはShobhadoshiが提供した試験問題資料は絶対あなたが試験に合格することを保証しますから。なんでそうやって言ったのはShobhadoshiが提供した試験問題資料は最新な資料ですから。

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