弊社の提供したのはあなたがほしいのだけではなく、試験のためにあなたの必要があるのです。あなたは決められないかもしれませんが、GoogleのProfessional-Data-Engineer試験復習のデモをダウンロードしてください。やってみて第一歩を進める勇気があります。 ITの専門者はGoogleのProfessional-Data-Engineer試験復習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。 問題集の全面性と権威性、GoogleのProfessional-Data-Engineer試験復習ソフトがPDF版、オンライン版とソフト版があるという資料のバーションの多様性、購入の前にデモの無料ダウンロード、購入の後でGoogleのProfessional-Data-Engineer試験復習ソフトの一年間の無料更新、これ全部は我々の誠の心を示しています。
Google Cloud Certified Professional-Data-Engineer 我々の商品にあなたを助けさせましょう。
Google Cloud Certified Professional-Data-Engineer試験復習 - Google Certified Professional Data Engineer Exam あなたはいつでもサブスクリプションの期間を延長することができますから、より多くの時間を取って充分に試験を準備できます。 弊社のProfessional-Data-Engineer ブロンズ教材問題集は大勢の専門家たちの努力で開発される成果です。初心者といい、数年IT仕事を従事した人といい、我々OmgzlookのGoogle Professional-Data-Engineer ブロンズ教材問題集は最良の選択であると考えられます。
Omgzlookはとても良い選択で、Professional-Data-Engineer試験復習の試験を最も短い時間に縮められますから、あなたの費用とエネルギーを節約することができます。それに、あなたに美しい未来を作ることに助けを差し上げられます。GoogleのProfessional-Data-Engineer試験復習認定試験に受かるのはあなたの技能を検証することだけでなく、あなたの専門知識を証明できて、上司は無駄にあなたを雇うことはしないことの証明書です。
Google Professional-Data-Engineer試験復習 - だから、私たちは信頼されるに値します。
Professional-Data-Engineer試験復習認定試験に合格することは難しいようですね。試験を申し込みたいあなたは、いまどうやって試験に準備すべきなのかで悩んでいますか。そうだったら、下記のものを読んでください。いまProfessional-Data-Engineer試験復習試験に合格するショートカットを教えてあげますから。あなたを試験に一発合格させる素晴らしいProfessional-Data-Engineer試験復習試験に関連する参考書が登場しますよ。それはOmgzlookのProfessional-Data-Engineer試験復習問題集です。気楽に試験に合格したければ、はやく試しに来てください。
もしあなたはまだ合格のためにGoogle Professional-Data-Engineer試験復習に大量の貴重な時間とエネルギーをかかって一生懸命準備し、Google Professional-Data-Engineer試験復習「Google Certified Professional Data Engineer Exam」認証試験に合格するの近道が分からなくって、今はOmgzlookが有効なGoogle Professional-Data-Engineer試験復習認定試験の合格の方法を提供して、君は半分の労力で倍の成果を取るの与えています。
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