PREP4SURE PROFESSIONAL-DATA-ENGINEER TEST DUMPS & PASS4SURE OF GOOGLE PROFESSIONAL-DATA-ENGINEER EXAM

Prep4sure Professional-Data-Engineer test dumps & pass4sure of Google Professional-Data-Engineer exam

Prep4sure Professional-Data-Engineer test dumps & pass4sure of Google Professional-Data-Engineer exam

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Google Professional-Data-Engineer Certification Exam comprises of multiple-choice and multiple-select questions that require a thorough understanding of Google Cloud Platform services such as BigQuery, Google Cloud Storage, and Google Cloud Dataflow. Professional-Data-Engineer exam also tests an individual's knowledge of data processing patterns and best practices, understanding of machine learning models and algorithms, and proficiency in designing and deploying solutions that meet business requirements.

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To prepare for the Google Professional-Data-Engineer certification exam, candidates must have a solid understanding of data engineering fundamentals, as well as knowledge of the Google Cloud Platform and its associated services. Candidates can use a variety of study resources, such as official Google Cloud Platform documentation, online courses, and practice exams, to prepare for the exam. Additionally, candidates should have experience working with data processing systems, data warehousing, and data analysis tools.

To prepare for the exam, prospective candidates can enroll in official GCP training courses or leverage various online resources such as practice exams, sample questions, and study materials. It's essential to have practical experience working with Google Cloud technologies to be able to pass the exam successfully. Professional-Data-Engineer Exam's duration is two hours, and the passing score is 70 percent. Once a candidate completes the exam and earns the certification, they gain access to exclusive professional networking opportunities and recognition as an industry expert in data engineering.

Google Certified Professional Data Engineer Exam Sample Questions (Q258-Q263):

NEW QUESTION # 258
How would you query specific partitions in a BigQuery table?

  • A. Use DATE BETWEEN in the WHERE clause
  • B. Use the EXTRACT(DAY) clause
  • C. Use the DAY column in the WHERE clause
  • D. Use the __PARTITIONTIME pseudo-column in the WHERE clause

Answer: D

Explanation:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02') Reference: https://cloud.google.com/bigquery/docs/partitioned-tables#the_partitiontime_pseudo_column


NEW QUESTION # 259
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?

  • A. Create a table called tracking_table and include a DATE column.
  • B. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
  • C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
  • D. Create a partitioned table called tracking_table and include a TIMESTAMP column.

Answer: D


NEW QUESTION # 260
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. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
  • B. 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.
  • C. 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.
  • D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

Answer: A


NEW QUESTION # 261
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.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)

  • A. Adjust the settings for each view to allow a related region-based security group view access.
  • B. Adjust the settings for each table to allow a related region-based security group view access.
  • C. Adjust the settings for each dataset to allow a related region-based security group view access.
  • D. Ensure each table is included in a dataset for a region.
  • E. Ensure all the tables are included in global dataset.

Answer: A,D


NEW QUESTION # 262
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

  • A. Spark
  • B. Fire
  • C. Blaze
  • D. Ignite

Answer: A

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/


NEW QUESTION # 263
......

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