GoogleのProfessional-Data-Engineer無料試験認証試験の合格証は多くのIT者になる夢を持つ方がとりたいです。でも、その試験はITの専門知識と経験が必要なので、合格するために一般的にも大量の時間とエネルギーをかからなければならなくて、助簡単ではありません。Goldmile-Infobizは素早く君のGoogle試験に関する知識を補充できて、君の時間とエネルギーが節約させるウェブサイトでございます。 我々IT専門かたちの作成するGoogleのProfessional-Data-Engineer無料試験ソフトを利用しているとき、あなたは自分の能力の高めを明らかに感じることができます。GoogleのProfessional-Data-Engineer無料試験試験は常に更新されていますから、あなたに一番新しい資料を提供するために、我々はご購入の後で一年間の無料更新サービスを提供してあなたに安心させます。 Goldmile-Infobizは異なるトレーニングツールと資源を提供してあなたのGoogleのProfessional-Data-Engineer無料試験の認証試験の準備にヘルプを差し上げます。
Google Cloud Certified Professional-Data-Engineer あなたは勇敢な人ですか。
我々社はGoogle Professional-Data-Engineer - Google Certified Professional Data Engineer Exam無料試験試験に準備するあなたに怖さを取り除き、正確の方法と問題集を提供できます。 Goldmile-Infobizへ来てあなたがほしいヘルパーと試験の準備ツールを見つけることができますから。Goldmile-Infobizの資料はきっとあなたがProfessional-Data-Engineer 資格トレーリング試験の認証資格を取ることを助けられます。
素晴らしい試験参考書です。Professional-Data-Engineer無料試験認定試験の難しさで近年、資格認定試験に合格した受験生はますます少なくなっていたと良く知られます。だから、我々社のIT専門家は長年にわたりGoogle Professional-Data-Engineer無料試験認定資格試験問題集作成に取り組んで、有効なProfessional-Data-Engineer無料試験試験問題集を書きました。
Google Professional-Data-Engineer無料試験 - まだなにを待っていますか。
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Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 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
Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?
A. Use the Google Cloud Billing API to see what account the warehouse is being billed to.
B. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
C. Get the identity and access management IIAM) policy of each table
D. Use Google Stackdriver Audit Logs to review data access.
Answer: B
QUESTION NO: 4
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: 5
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C
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Updated: May 27, 2022