GoogleのProfessional-Data-Engineerテスト参考書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。 Goldmile-Infobizの素晴らしい問題集はIT技術者が長年を重ねて、総括しました経験と結果です。先人の肩の上に立って、あなたも成功に一歩近付くことができます。 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テスト参考書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 もちろんです。
Google Cloud Certified Professional-Data-Engineerテスト参考書 - Google Certified Professional Data Engineer Exam 弊社は100%合格率を保証し、購入前にネットでダウンロードしてください。 もっと大切なのは、あなたもより多くの仕事のスキルをマスターしたことを証明することができます。では、はやくGoogleのProfessional-Data-Engineer 専門知識訓練認定試験を受験しましょう。
この情報の時代には、IT業界にとても注目され、この強い情報技術業界にIT人材が得難いです。こうしてGoogle認定試験がとても重要になります。でも、この試験がとても難しくてIT者になりたい方が障害になっています。
Google Professional-Data-Engineerテスト参考書 - 信じられなら利用してみてください。
Goldmile-Infobizの商品を使用したあとのひとはGoldmile-Infobizの商品がIT関連認定試験に対して役に立つとフィードバックします。弊社が提供した商品を利用すると試験にたやすく合格しました。GoogleのProfessional-Data-Engineerテスト参考書認証試験に関する訓練は対応性のテストで君を助けることができて、試験の前に十分の準備をさしあげます。
Goldmile-Infobizの問題集は100％の合格率を持っています。Goldmile-InfobizのProfessional-Data-Engineerテスト参考書問題集は多くのIT専門家の数年の経験の結晶で、高い価値を持っています。
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
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: 3
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: 4
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
QUESTION NO: 5
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
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Updated: May 27, 2022