최신버전Professional-Data-Engineer최신버전인기덤프자료덤프는Google Certified Professional Data Engineer Exam시험패스의지름길

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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Data Engineer Exam
Exam Number:	Professional-Data-Engineer
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Related Certifications:	Google Cloud Associate Cloud Engineer Google Cloud Professional Data Analyst Google Cloud Professional Cloud Architect
Available Languages:	Japanese, English
Real Exam Qty:	40 - 50
Passing Score:	700 / 1000
Exam Price:	USD 200 (plus tax where applicable)
Exam Format:	Multiple select, Multiple choice
Recommended Training:	Official Exam Guide Google Cloud Professional Data Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online-proctored or onsite-proctored
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

>> Professional-Data-Engineer최신버전 인기 덤프자료 <<

Google Professional-Data-Engineer최고품질 덤프문제모음집 & Professional-Data-Engineer높은 통과율 시험대비 덤프공부

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Google Professional-Data-Engineer 자격증 시험을 준비하기 위해서는 BigQuery, Cloud Dataflow, Cloud Dataproc 및 Cloud Pub/Sub와 같은 GCP 데이터 엔지니어링 서비스를 실제로 사용해 본 경험이 있어야 합니다. 또한 데이터 모델링, 데이터 웨어하우징 및 머신 러닝 경험이 필요합니다. Google은 시험을 볼 전에 Coursera의 Google Cloud Platform에서 데이터 엔지니어링 전문화를 수강하는 것을 권장합니다.

최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q34-Q39):

질문 # 34
You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Schedule the DAGs to run hourly.
B. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.
C. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Schedule the DAG to run hourly.
D. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.

정답：B

질문 # 35
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

A. Retry the query with exponential backoff, up to a cap of 15 minutes.
B. Reduce the query frequency to once every hour until the database comes back online.
C. Issue a command to restart the database servers.
D. Retry the query every second until it comes back online to minimize staleness of data.

정답：A

질문 # 36
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your dat a. What should you do?

A. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
B. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
C. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.
D. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

정답：C

질문 # 37
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

A. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
B. Place the MariaDB instances in an Instance Group with a Health Check.
C. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
D. Install the StackDriver Agent and configure the MySQL plugin.

정답：A

설명：
The GitHub repository named google-fluentd-catch-all-config which includes the configuration files for the Logging agent for ingesting the logs from various third-party software packages.

질문 # 38
You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

A. Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets
B. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery
C. Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink
D. Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

정답：D

질문 # 39
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Professional-Data-Engineer최고품질 덤프문제모음집: https://www.itdumpskr.com/Professional-Data-Engineer-exam.html

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