Google Professional-Data-Engineer全真模擬試験認証試験に合格することが簡単ではなくて、Google Professional-Data-Engineer全真模擬試験証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。今競争の激しいIT業界で地位を固めたいですが、Google Professional-Data-Engineer全真模擬試験認証試験に合格しなければなりません。 Goldmile-Infobizの専門家チームは彼らの経験と知識を利用して長年の研究をわたって多くの人は待ちに待ったGoogleのProfessional-Data-Engineer全真模擬試験「Google Certified Professional Data Engineer Exam」認証試験について教育資料が完成してから、大変にお客様に歓迎されます。Goldmile-Infobizの模擬試験は真実の試験問題はとても似ている専門家チームの勤労の結果としてとても値打ちがあります。 Goldmile-Infobizは受験者に向かって試験について問題を解決する受験資源を提供するサービスのサイトで、さまざまな受験生によって別のトレーニングコースを提供いたします。
Google Cloud Certified Professional-Data-Engineer あなたは最高のトレーニング資料を手に入れました。
Goldmile-InfobizのGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam全真模擬試験試験トレーニング資料は全てのオンラインのトレーニング資料で一番よいものです。 それは問題ではないですよ。あなたはGoldmile-InfobizのProfessional-Data-Engineer テスト参考書問題集を利用することができますから。
人生には様々な選択があります。選択は必ずしも絶対な幸福をもたらさないかもしれませんが、あなたに変化のチャンスを与えます。Goldmile-InfobizのGoogleのProfessional-Data-Engineer全真模擬試験「Google Certified Professional Data Engineer Exam」試験トレーニング資料はIT職員としてのあなたがIT試験に受かる不可欠なトレーニング資料です。
Google Professional-Data-Engineer全真模擬試験認定試験はたいへん難しい試験ですね。
Goldmile-Infobizは専門的で、たくさんの受験生のために、君だけのために存在するのです。それは正確的な試験の内容を保証しますし、良いサービスで、安い価格で営業します。Goldmile-Infobizがあれば、GoogleのProfessional-Data-Engineer全真模擬試験試験に合格するのは心配しません。Goldmile-Infobizは君が最も早い時間でGoogleのProfessional-Data-Engineer全真模擬試験試験に合格するのを助けます。私たちは君がITエリートになるのに頑張ります。
多くのサイトの中で、どこかのGoogleのProfessional-Data-Engineer全真模擬試験試験問題集は最も正確性が高いですか。無論Goldmile-InfobizのGoogleのProfessional-Data-Engineer全真模擬試験問題集が一番頼りになります。
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 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: 3
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: 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