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Google Cloud Certified Professional-Data-Engineer 成功の楽園にどうやって行きますか。
Google Cloud Certified Professional-Data-Engineer参考書勉強 - Google Certified Professional Data Engineer Exam 編成チュートリアルは授業コース、実践検定、試験エンジンと一部の無料なPDFダウンロードを含めています。 Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。真実かつ信頼性の高いものだからこそ、Goldmile-Infobizの試験参考書は長い時間にわたってますます人気があるようになっています。
それも我々が全てのお客様に対する約束です。あなたはこのような人々の一人ですか。さまざまな資料とトレーニング授業を前にして、どれを選ぶか本当に困っているのです。
Google Professional-Data-Engineer参考書勉強 - それはあなたが夢を実現することを助けられます。
あなたはどのような方式で試験を準備するのが好きですか。PDF、オンライン問題集または模擬試験ソフトですか。我々Goldmile-Infobizはこの3つを提供します。すべては購入した前で無料でデモをダウンロードできます。ふさわしい方式を選ぶのは一番重要なのです。どの版でもGoogleのProfessional-Data-Engineer参考書勉強試験の復習資料は効果的なのを保証します。
試験に合格してから、あなたのキャリアは美しい時期を迎えるようになります。偉大な事業を実現するために信心を持つ必要があります。
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
現在あなたに提供するのは大切なGoogleのJuniper JN0-650資料です。 そして、ソフトウェア版のMicrosoft MS-102問題集は実際試験の雰囲気を感じさせることができます。 これなので、今から我々社Goldmile-InfobizのLinux Foundation CNPA試験に合格するのに努力していきます。 Goldmile-InfobizのHP HPE7-A03試験参考書は他のHP HPE7-A03試験に関連するする参考書よりずっと良いです。 どのようにGoogle ServiceNow CIS-SPM-JPN試験に準備すると悩んでいますか。
Updated: May 27, 2022