Professional-Data-Engineer日本語練習問題、Google Professional-Data-Engineer問題と解答 & Google Certified Professional-Data-Engineer Exam - Omgzlook

なぜ受験生のほとんどはOmgzlookを選んだのですか。それはOmgzlookがすごく便利で、広い通用性があるからです。OmgzlookのITエリートたちは彼らの専門的な目で、最新的なGoogleのProfessional-Data-Engineer日本語練習問題試験トレーニング資料に注目していて、うちのGoogleのProfessional-Data-Engineer日本語練習問題問題集の高い正確性を保証するのです。 Omgzlookは君の早くGoogleのProfessional-Data-Engineer日本語練習問題認定試験に合格するために、きみのもっと輝い未来のために、君の他人に羨ましいほど給料のために、ずっと努力しています。長年の努力を通じて、OmgzlookのGoogleのProfessional-Data-Engineer日本語練習問題認定試験の合格率が100パーセントになっていました。 OmgzlookのGoogleのProfessional-Data-Engineer日本語練習問題問題集を購入するなら、君がGoogleのProfessional-Data-Engineer日本語練習問題認定試験に合格する率は100パーセントです。

Google Cloud Certified Professional-Data-Engineer 正しい方法は大切です。

Google Cloud Certified Professional-Data-Engineer日本語練習問題 - Google Certified Professional Data Engineer Exam あなたが試験に合格するのは我々への一番よい評価です。 Omgzlookは多くの受験生を助けて彼らにGoogleのProfessional-Data-Engineer ソフトウエア試験に合格させることができるのは我々専門的なチームがGoogleのProfessional-Data-Engineer ソフトウエア試験を研究して解答を詳しく分析しますから。試験が更新されているうちに、我々はGoogleのProfessional-Data-Engineer ソフトウエア試験の資料を更新し続けています。

その中の一部は暇な時間だけでGoogleのProfessional-Data-Engineer日本語練習問題試験を準備します。我々Omgzlookが数年以来商品の開発をしている目的はIT業界でよく発展したい人にGoogleのProfessional-Data-Engineer日本語練習問題試験に合格させることです。GoogleのProfessional-Data-Engineer日本語練習問題試験のための資料がたくさんありますが、Omgzlookの提供するのは一番信頼できます。

Google Professional-Data-Engineer日本語練習問題 - PayPalは国際的に最大の安全的な支払システムです。

OmgzlookのGoogle Professional-Data-Engineer日本語練習問題問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。我々Professional-Data-Engineer日本語練習問題問題集の通過率は高いので、90%の合格率を保証します。あなたは弊社の高品質Google Professional-Data-Engineer日本語練習問題試験資料を利用して、一回に試験に合格します。

弊社のProfessional-Data-Engineer日本語練習問題真題を入手して、試験に合格する可能性が大きくなります。社会と経済の発展につれて、多くの人はIT技術を勉強します。

Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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

QUESTION NO: 4
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: 5
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
B. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
D. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
Answer: C

努力すれば報われますなので、Google HP HPE0-J68資格認定を取得して自分の生活状況を改善できます。 我々Google Professional-Data-Engineer問題集を利用し、試験に参加しましょう。 そして、ECCouncil 212-82-JPN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。 あなたはまだ躊躇しているなら、OmgzlookのIIA IIA-CIA-Part2-KR問題集デモを参考しましょ。 Dell D-DLM-A-01 - この試験に合格すれば君の専門知識がとても強いを証明し得ます。

Updated: May 27, 2022