すべては豊富な内容があって各自のメリットを持っています。あなたは各バーションのGoogleのProfessional-Data-Engineer資格試験試験の資料をダウンロードしてみることができ、あなたに一番ふさわしいバーションを見つけることができます。暇な時間だけでGoogleのProfessional-Data-Engineer資格試験試験に合格したいのですか。 Omgzlook は世界的によく知られているサイトです。どうしてこのような大きな連鎖反応になりましたか。 我々OmgzlookはGoogleのProfessional-Data-Engineer資格試験試験問題集をリリースする以降、多くのお客様の好評を博したのは弊社にとって、大変な名誉なことです。
Google Cloud Certified Professional-Data-Engineer しかも、楽に試験に合格することができます。
GoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam資格試験試験に合格することは容易なことではなくて、良い訓練ツールは成功の保証でOmgzlookは君の試験の問題を準備してしまいました。 もし学習教材は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。OmgzlookのGoogleのProfessional-Data-Engineer 受験対策試験トレーニング資料は正確性が高くて、カバー率も広い。
どんな業界で自分に良い昇進機会があると希望する職人がとても多いと思って、IT業界にも例外ではありません。ITの専門者はGoogleのProfessional-Data-Engineer資格試験認定試験があなたの願望を助けって実現できるのがよく分かります。Omgzlookはあなたの夢に実現させるサイトでございます。
Google Professional-Data-Engineer資格試験認定試験に合格することは難しいようですね。
明日ではなく、今日が大事と良く知られるから、そんなにぐずぐずしないで早く我々社のGoogle Professional-Data-Engineer資格試験日本語対策問題集を勉強し、自身を充実させます。我々社の練習問題は長年でProfessional-Data-Engineer資格試験全真模擬試験トレーニング資料に研究している専業化チームによって編集されます。Google Professional-Data-Engineer資格試験資格問題集はPDF版、ソフト版、オンライン版を含まれ、この三つバージョンから自分の愛用することを選んでいます。他の人に先立ってGoogle Professional-Data-Engineer資格試験認定資格を得るために、今から勉強しましょう。
Omgzlookは君にとってベストな選択になります。ここには、私たちは君の需要に応じます。
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
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