Professional-Data-Engineer問題トレーリング、Professional-Data-Engineer絶対合格 - Google Professional-Data-Engineer受験対策解説集 - Goldmile-Infobiz

Professional-Data-Engineer問題トレーリング問題集を購入すれば、あなたはいつでもどこでも勉強することができます。Professional-Data-Engineer問題トレーリング問題集はIT専門家が長年の研究したことです。従って、高品質で、Professional-Data-Engineer問題トレーリング試験の合格率が高いです。 Goldmile-Infobizが提供したGoogleのProfessional-Data-Engineer問題トレーリング「Google Certified Professional Data Engineer Exam」試験問題と解答が真実の試験の練習問題と解答は最高の相似性があり、一年の無料オンラインの更新のサービスがあり、100%のパス率を保証して、もし試験に合格しないと、弊社は全額で返金いたします。 あなたは心配する必要がないです。

Google Cloud Certified Professional-Data-Engineer Goldmile-Infobizというサイトです。

Goldmile-Infobizが提供したGoogleのProfessional-Data-Engineer - Google Certified Professional Data Engineer Exam問題トレーリング「Google Certified Professional Data Engineer Exam」試験問題と解答が真実の試験の練習問題と解答は最高の相似性があり、一年の無料オンラインの更新のサービスがあり、100%のパス率を保証して、もし試験に合格しないと、弊社は全額で返金いたします。 Goldmile-InfobizのGoogleのProfessional-Data-Engineer 試験問題解説集試験トレーニング資料は必要とするすべての人に成功をもたらすことができます。GoogleのProfessional-Data-Engineer 試験問題解説集試験は挑戦がある認定試験です。

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Google Professional-Data-Engineer問題トレーリング - もちろんです。

Goldmile-Infobizの商品は100%の合格率を保証いたします。Goldmile-InfobizはITに対応性研究続けて、高品質で低価格な問題集が開発いたしました。Goldmile-Infobizの商品の最大の特徴は20時間だけ育成課程を通して楽々に合格できます。

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Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
Answer: D
Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud
Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your
Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

QUESTION NO: 2
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?
A. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.
B. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to
GCS.
C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a
Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
D. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
Answer: B

QUESTION NO: 3
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud Dataflow
Answer: C
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive

QUESTION NO: 4
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
A. Continuously retrain the model on just the new data.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the new data while using the existing data as your test set.
D. Train on the existing data while using the new data as your test set.
Answer: C

QUESTION NO: 5
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
A. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
C. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C

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Updated: May 27, 2022