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Professional-Data-Engineer最新受験攻略 & Professional-Data-Engineer試験資料 - Professional-Data-Engineer的中合格問題集 - Royalholidayclubbed
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そして、Professional-Data-Engineer最新受験攻略試験参考書の問題は本当の試験問題とだいたい同じことであるとわかります。Professional-Data-Engineer最新受験攻略試験参考書があれば,ほかの試験参考書を勉強する必要がないです。多分、Professional-Data-Engineer最新受験攻略テスト質問の数が伝統的な問題の数倍である。 これはあなたに安心で弊社の商品を購入させるためです。あなたはGoogleのProfessional-Data-Engineer最新受験攻略試験を準備しているとき、あなたの時間とお金を無駄にしないであなたに試験に一番有効な助けを提供するのは我々がGoogleのProfessional-Data-Engineer最新受験攻略ソフトを作成する達成したい目標です。 GoogleのProfessional-Data-Engineer最新受験攻略の認定試験は君の実力を考察するテストでございます。
Google Cloud Certified Professional-Data-Engineer きっと君に失望させないと信じています。
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Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C
QUESTION NO: 2
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
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
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 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
Microsoft AI-900-CN - でも多くの人が合格するために大量の時間とエネルギーをかかって、無駄になります。 ISTQB CTAL-TM-KR - それは正確性が高くて、カバー率も広いです。 RoyalholidayclubbedはGoogleのAPI API-936の認定試験を真実に、全面的に研究したサイトです。 もちろん、我々はあなたに一番安心させるのは我々の開発する多くの受験生に合格させるGoogleのSAP C_BCSBS_2502試験のソフトウェアです。 Google Professional-Cloud-Security-Engineer-JPN - RoyalholidayclubbedはIT領域の10年以上の認定経験を持っていますから、問題と解答に含まれています。
Updated: May 27, 2022
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