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Google Cloud Certified Professional-Data-Engineer 明るい未来を準備してあげます。
Google Cloud Certified Professional-Data-Engineer科目対策 - Google Certified Professional Data Engineer Exam このような素晴らしい資料をぜひ見逃さないでください。 Omgzlookというサイトは素晴らしいソースサイトで、GoogleのProfessional-Data-Engineer ダウンロードの試験材料、研究材料、技術材料や詳しい解答に含まれています。問題集が提供したサイトは近年で急速に増加しています。
あるいは、無料で試験Professional-Data-Engineer科目対策問題集を更新してあげるのを選択することもできます。こんな保障がありますから、心配する必要は全然ないですよ。OmgzlookのProfessional-Data-Engineer科目対策問題集は多くの受験生に検証されたものですから、高い成功率を保証できます。
Google Professional-Data-Engineer科目対策 - そうすると、我々の信頼性をテストできます。
なぜ受験生のほとんどはOmgzlookを選んだのですか。それはOmgzlookがすごく便利で、広い通用性があるからです。OmgzlookのITエリートたちは彼らの専門的な目で、最新的なGoogleのProfessional-Data-Engineer科目対策試験トレーニング資料に注目していて、うちのGoogleのProfessional-Data-Engineer科目対策問題集の高い正確性を保証するのです。もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、Omgzlookは無料でサンプルを提供することができます。
OmgzlookのGoogleのProfessional-Data-Engineer科目対策試験トレーニング資料はIT認証試験を受ける人々の必需品です。このトレーニング資料を持っていたら、試験のために充分の準備をすることができます。
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
OmgzlookのGoogleのEMC D-MSS-DS-23問題集を購入するなら、君がGoogleのEMC D-MSS-DS-23認定試験に合格する率は１００パーセントです。 Microsoft MS-700-JPN - この試験の認証資格を取ったら、あなたは多くの利益を得ることができます。 弊社のGoogleのSplunk SPLK-1002Jソフトを購入するのを決めるとき、我々は各方面であなたに保障を提供します。 Fortinet NSE5_FSM-6.3 - 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。 Omgzlookは多くの受験生を助けて彼らにGoogleのCompTIA 220-1102J試験に合格させることができるのは我々専門的なチームがGoogleのCompTIA 220-1102J試験を研究して解答を詳しく分析しますから。
Updated: May 27, 2022