暇の時間を利用して勉強します。努力すれば報われますなので、Google Professional-Data-Engineer資格認定試験資格認定を取得して自分の生活状況を改善できます。IT職員のあなたは毎月毎月のあまり少ない給料を持っていますが、暇の時間でひたすら楽しむんでいいですか。 GoogleのProfessional-Data-Engineer資格認定試験ソフトを使用するすべての人を有効にするために最も快適なレビュープロセスを得ることができ、我々は、GoogleのProfessional-Data-Engineer資格認定試験の資料を提供し、PDF、オンラインバージョン、およびソフトバージョンを含んでいます。あなたの愛用する版を利用して、あなたは簡単に最短時間を使用してGoogleのProfessional-Data-Engineer資格認定試験試験に合格することができ、あなたのIT機能を最も権威の国際的な認識を得ます! 現在の社会で、Professional-Data-Engineer資格認定試験試験に参加する人がますます多くなる傾向があります。
Google Cloud Certified Professional-Data-Engineer 無事試験に合格しました。
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Google Professional-Data-Engineer資格認定試験試験は難しいです。
今の多くのIT者が参加している試験に、GoogleのProfessional-Data-Engineer資格認定試験認定試験「Google Certified Professional Data Engineer Exam」がとても人気がある一つとして、合格するために豊富な知識と経験が必要です。GoogleのProfessional-Data-Engineer資格認定試験認定試験に準備する練習ツールや訓練機関に通学しなればまりませんでしょう。Goldmile-Infobizは君のもっともよい選択ですよ。多くIT者になりたい方にGoogleのProfessional-Data-Engineer資格認定試験認定試験に関する問題集を準備しております。君に短い時間に大量のITの専門知識を補充させています。
Google Professional-Data-Engineer資格認定試験試験の合格のために、Goldmile-Infobizを選択してください。Goldmile-InfobizはGoogleのProfessional-Data-Engineer資格認定試験「Google Certified Professional Data Engineer Exam」試験に関する完全な資料を唯一のサービスを提供するサイトでございます。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 4
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: 5
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
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Updated: May 27, 2022