Goldmile-InfobizのGoogleのProfessional-Data-Engineer日本語講座試験トレーニング資料は現在で一番人気があるダウンロードのフォーマットを提供します。PDFとソフトのフォーマットで、ダウンロードするのは易いです。Goldmile-Infobizが提供した製品がIT専門家は実際の経験を活かして作った最も良い製品で、あなたが自分の目標を達成するようにずっと一生懸命頑張っています。 もしGoldmile-Infobizの学習教材を購入した後、どんな問題があれば、或いは試験に不合格になる場合は、私たちが全額返金することを保証いたします。Goldmile-Infobizを信じて、私たちは君のそばにいるから。 それはあなたが夢を実現することを助けられます。
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そして、ソフトウェア版のProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam日本語講座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Professional-Data-Engineer 的中合格問題集試験に合格するのに努力していきます。弊社のGoogleのProfessional-Data-Engineer 的中合格問題集真題によって、資格認定証明書を受け取れて、仕事の昇進を実現できます。
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Google Professional-Data-Engineer日本語講座試験の困難度なので、試験の準備をやめます。実には、正確の方法と資料を探すなら、すべては問題ではりません。我々社はGoogle Professional-Data-Engineer日本語講座試験に準備するあなたに怖さを取り除き、正確の方法と問題集を提供できます。ご購入の前後において、いつまでもあなたにヘルプを与えられます。あなたのGoogle Professional-Data-Engineer日本語講座試験に合格するのは我々が与えるサプライズです。
自分のレベルを高めたいですか。では、仕事に役に立つスキルをもっと身に付けましょう。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 4
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: 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