PROFESSIONAL-DATA-ENGINEER VALID TEST TUTORIAL | RELIABLE PROFESSIONAL-DATA-ENGINEER EXAM SYLLABUS

Professional-Data-Engineer Valid Test Tutorial | Reliable Professional-Data-Engineer Exam Syllabus

Professional-Data-Engineer Valid Test Tutorial | Reliable Professional-Data-Engineer Exam Syllabus

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Google Professional-Data-Engineer certification exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer Exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.

Google Certified Professional Data Engineer Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

  • A. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
  • B. Manually start the Cloud Dataflow job each morning when you get into the office.
  • C. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
  • D. Change the processing job to use Google Cloud Dataproc instead.

Answer: A


NEW QUESTION # 20
Which of these is not a supported method of putting data into a partitioned table?

  • A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
  • B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
  • C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
    "Partitioned".
  • D. Create a partitioned table and stream new records to it every day.

Answer: C

Explanation:
Explanation
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch.
Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


NEW QUESTION # 21
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

  • A. Create a BigQuery table snapshot on a daily basis.
  • B. Access historical data by using time travel in BigQuery.
  • C. Export the data from BigQuery into a new table that excludes the corrupted data.
  • D. Migrate your data to multi-region BigQuery buckets.

Answer: B

Explanation:
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
Reference:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps


NEW QUESTION # 22
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's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000
installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud
Dataflow pipeline configuration setting should you update?

  • A. The zone
  • B. The maximum number of workers
  • C. The number of workers
  • D. The disk size per worker

Answer: A


NEW QUESTION # 23
You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data for an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of dat a. What should you do?

  • A. Ask the source system to re-push the data to Pub/Sub, and subscribe to it.
  • B. Set the subscriber retention policy to 30 days.
  • C. Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.
  • D. Set the topic retention policy to 30 days.

Answer: D

Explanation:
By setting the topic retention policy to 30 days, you can ensure that any new subscriber can access the messages that were published to the topic within the last 30 days1. This feature allows you to replay previously acknowledged messages or initialize new subscribers with historical data2. You can configure the topic retention policy by using the Cloud Console, the gcloud command-line tool, or the Pub/Sub API1.
Option A is not efficient, as it requires creating a new topic and duplicating the data for each new subscriber, which would increase the storage costs and complexity. Option C is not effective, as it only affects the unacknowledged messages in a subscription, and does not allow new subscribers to access older data3. Option D is not feasible, as it depends on the source system's ability and willingness to re-push the data, and it may cause data duplication or inconsistency. Reference:
1: Create a topic | Cloud Pub/Sub Documentation | Google Cloud
2: Replay and purge messages with seek | Cloud Pub/Sub Documentation | Google Cloud
3: When is a PubSub Subscription considered to be inactive?


NEW QUESTION # 24
......

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