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Google Cloud Certified Professional-Data-Engineer これも現代社会が圧力に満ちている一つの反映です。

Google Professional-Data-Engineer - Google Certified Professional Data Engineer Exam専門知識訓練「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 - Google Certified Professional Data Engineer Exam専門知識訓練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。 GoogleのProfessional-Data-Engineer 模擬対策認定試験を受験するために準備をしているあなたは、NewValidDumpsという成功できるチャンスを掴みましたか。NewValidDumpsのProfessional-Data-Engineer 模擬対策問題集はあなたが楽に試験に合格する保障です。

今の社会の中で、ネット上で訓練は普及して、弊社は試験問題集を提供する多くのネットの一つでございます。NewValidDumpsが提供したのオンライン商品がIT業界では品質の高い学習資料、受験生の必要が満足できるサイトでございます。

Google Professional-Data-Engineer専門知識訓練 - 心配する必要はないです。

NewValidDumpsは実際の環境で本格的なGoogleのProfessional-Data-Engineer専門知識訓練「Google Certified Professional Data Engineer Exam」の試験の準備過程を提供しています。もしあなたは初心者若しくは専門的な技能を高めたかったら、NewValidDumpsのGoogleのProfessional-Data-Engineer専門知識訓練「Google Certified Professional Data Engineer Exam」の試験問題があなたが一歩一歩自分の念願に近くために助けを差し上げます。試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。しかも、一年間の無料更新サービスを提供します。

がむしゃらに試験に要求された関連知識を積み込むより、価値がある問題を勉強したほうがいいです。効率のあがる試験問題集は受験生の皆さんにとって欠くことができないツールです。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 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

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Updated: May 27, 2022