多くの人々はGoogleのProfessional-Data-Engineer資格問題集試験に合格できるのは難しいことであると思っています。この悩みに対して、我々社Goldmile-InfobizはGoogleのProfessional-Data-Engineer資格問題集試験に準備するあなたに専門的なヘルプを与えられます。弊社のGoogleのProfessional-Data-Engineer資格問題集練習問題を利用したら、あなたは気楽に勉強するだけではなく、順調に試験に合格します。 君がGoogleのProfessional-Data-Engineer資格問題集問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もし学習教材は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。 勉強中で、何の質問があると、メールで我々はあなたのためにすぐ解決します。
Professional-Data-Engineer資格問題集問題集の特徴は便利で使い安いです。
Goldmile-InfobizのGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam資格問題集試験トレーニング資料は全てのオンラインのトレーニング資料で一番よいものです。 そのデモはProfessional-Data-Engineer 受験対策試験資料の一部を含めています。私たちは本当にお客様の貴重な意見をProfessional-Data-Engineer 受験対策試験資料の作りの考慮に入れます。
人生には様々な選択があります。選択は必ずしも絶対な幸福をもたらさないかもしれませんが、あなたに変化のチャンスを与えます。Goldmile-InfobizのGoogleのProfessional-Data-Engineer資格問題集「Google Certified Professional Data Engineer Exam」試験トレーニング資料はIT職員としてのあなたがIT試験に受かる不可欠なトレーニング資料です。
Google Professional-Data-Engineer資格問題集 - Goldmile-Infobizには専門的なエリート団体があります。
もし君がGoogleのProfessional-Data-Engineer資格問題集に参加すれば、良い学習のツルを選ぶすべきです。GoogleのProfessional-Data-Engineer資格問題集認定試験はIT業界の中でとても重要な認証試験で、合格するために良い訓練方法で準備をしなければなりません。。
もし君の予算がちょっと不自由で、おまけに質の良いGoogleのProfessional-Data-Engineer資格問題集試験トレーニング資料を購入したいなら、Goldmile-InfobizのGoogleのProfessional-Data-Engineer資格問題集試験トレーニング資料を選択したほうが良いです。それは値段が安くて、正確性も高くて、わかりやすいです。
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 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: 4
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: 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