私たちの会社は、コンテンツだけでなくディスプレイ上でも、Professional-Data-Engineer試験対策試験材料の設計に最新の技術を採用しています。激しく変化する世界に対応し、私たちのProfessional-Data-Engineer試験対策試験資料のガイドで、あなたの長所を発揮することができます。 また、あなたも私たちのProfessional-Data-Engineer試験対策試験資料を使って、個人的に重要な知識を集約し、自分の需要によって、Professional-Data-Engineer試験対策試験のために様々な勉強方法を選ぶことができます。 Google Professional-Data-Engineer試験対策「Google Certified Professional Data Engineer Exam」認証試験に合格することが簡単ではなくて、Google Professional-Data-Engineer試験対策証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。 弊社のIT技術専門家たち は質が高い問題集と答えを提供し、お客様が合格できるように努めています。
では、はやくGoogleのProfessional-Data-Engineer試験対策認定試験を受験しましょう。我々の提供するPDF版のGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam試験対策試験の資料はあなたにいつでもどこでも読めさせます。 RoyalholidayclubbedのProfessional-Data-Engineer 試験勉強過去問問題集が最高のツールです。この問題集には試験の優秀な過去問が集められ、しかも最新のシラバスに従って出題される可能性がある新しい問題も追加しました。
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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
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