적중율좋은Professional-Data-Engineer인기시험덤프Google Certified Professional Data Engineer Exam시험대비자료

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IT자격증을 많이 취득하여 IT업계에서 자신만의 단단한 자리를 보장하는것이 여러분들의 로망이 아닐가 싶습니다. Itexamdump의 완벽한 Google인증 Professional-Data-Engineer덤프는 IT전문가들이 자신만의 노하우와 경험으로 실제Google인증 Professional-Data-Engineer시험문제에 대비하여 연구제작한 완벽한 작품으로서 100%시험통과율을 보장합니다.

Google Professional-Data-Engineer 시험은 객관식 및 다중 선택 문제, 그리고 후보자가 문제 해결 능력을 입증해야하는 시나리오 기반 문제로 구성됩니다. 시험 시간은 3 시간이며, 후보자는 최소 70 % 이상의 점수를 획득해야합니다. 시험 수수료는 200 달러이며 영어, 일본어 및 스페인어로 제공됩니다.

이 시험은 객관식 질문으로 구성되며 응시자는 다양한 데이터 엔지니어링 개념 및 기술에 대한 지식을 보여 주어야합니다. 이 질문은 Google Cloud 플랫폼의 도구 및 서비스를 사용하여 데이터를 분석하고 데이터 변환 및 데이터 처리 솔루션을 구현하는 후보자의 기능을 테스트하도록 설계되었습니다. 시험의 길이는 2 시간이며, 후보자는 인증을 받기 위해 70% 이상의 합격 점수를 달성해야합니다.

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Professional-Data-Engineer최신 인증시험 기출문제 - Professional-Data-Engineer시험패스

만약 아직도 우리를 선택할지에 대하여 망설이고 있다면. 우선은 우리 사이트에서 Itexamdump가 제공하는 무료인 일부 문제와 답을 다운하여 체험해보시고 결정을 내리시길 바랍니다.그러면 우리의 덤프에 믿음이;갈 것이고,우리 또한 우리의 문제와 답들은 무조건 100%통과 율로 아주 고득점으로Google인증Professional-Data-Engineer험을 패스하실 수 있습니다,

최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q178-Q183):

질문 # 178
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

정답：A

질문 # 179
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.
B. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.

정답：B

질문 # 180
You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

A. Create one unique table for all of the indices, and then use the index and timestamp as the row key design
B. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.
C. For each index, have a separate table and use a timestamp as the row key design
D. For each index, have a separate table and use a reverse timestamp as the row key design

정답：A

질문 # 181
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model', table . How should you create the ML pipeline?
user_features)

A. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
B. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
C. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.

정답：A

질문 # 182
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the TABLE_DATE_RANGEfunction
B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D. Use the WHERE_PARTITIONTIMEpseudo column

정답：A

설명：
Explanation/Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understand- your-mobile-app?hl=am

질문 # 183
......

Google Professional-Data-Engineer인증시험패스에는 많은 방법이 있습니다. 먼저 많은 시간을 투자하고 신경을 써서 전문적으로 과련 지식을 터득한다거나; 아니면 적은 시간투자와 적은 돈을 들여 Itexamdump의 인증시험덤프를 구매하는 방법 등이 있습니다.

Professional-Data-Engineer최신 인증시험 기출문제: https://www.itexamdump.com/Professional-Data-Engineer.html