UPDATED PROFESSIONAL-DATA-ENGINEER EXAM TEST OFFER YOU ACCURATE LATEST EXAM VCE | GOOGLE GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM

Updated Professional-Data-Engineer Exam Test offer you accurate Latest Exam Vce | Google Google Certified Professional Data Engineer Exam

Updated Professional-Data-Engineer Exam Test offer you accurate Latest Exam Vce | Google Google Certified Professional Data Engineer Exam

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The Google Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions that assess the candidate's knowledge and skills in areas such as data ingestion, transformation, and storage on Google Cloud Platform. Professional-Data-Engineer exam also covers topics such as data processing using Apache Beam, data analysis using BigQuery, and machine learning using TensorFlow. Candidates who pass the exam will receive the Google Certified Professional Data Engineer certification, which is widely recognized in the industry as a mark of expertise in designing and implementing data solutions on Google Cloud Platform.

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Google Certified Professional Data Engineer Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

  • A. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
  • B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
  • C. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
  • D. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

Answer: D

Explanation:
https://cloud.google.com/sql/docs/mysql/high-availability


NEW QUESTION # 219
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

  • A. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
  • B. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
  • C. Export the information to Cloud Stackdriver, and set up an Alerting policy
  • D. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Answer: A

Explanation:
Explanation/Reference:


NEW QUESTION # 220
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

  • A. Cloud Datastore and Cloud Bigtable
  • B. BigQuery and Cloud Storage
  • C. BigQuery and Cloud Bigtable
  • D. Cloud Bigtable and Cloud SQL

Answer: C


NEW QUESTION # 221
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 StackDriver Agent and configure the MySQL plugin.
  • D. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Answer: A

Explanation:
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.


NEW QUESTION # 222
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

  • A. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
  • B. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
  • C. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
  • D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Answer: A

Explanation:
Avro is compressed format and dataflow for parallel pipeline and bigquery for storage.


NEW QUESTION # 223
......

The web-based Professional-Data-Engineer mock test is compatible with Chrome, Firefox, Internet Explorer, MS Edge, Opera, Safari, and others. This version of the Google Professional-Data-Engineer practice exam requires an active internet connection. It does not require any additional plugins or software installation to operate. Furthermore, Android, iOS, Windows, Mac, and Linux support the Google Professional-Data-Engineer web-based practice exam. Features of the EXAM CODE desktop practice exam software are web-based as well.

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