PROFESSIONAL-DATA-ENGINEER EXAM GUIDE, EXAM VCE PROFESSIONAL-DATA-ENGINEER FREE

Professional-Data-Engineer Exam Guide, Exam Vce Professional-Data-Engineer Free

Professional-Data-Engineer Exam Guide, Exam Vce Professional-Data-Engineer Free

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Google Certified Professional Data Engineer Exam Sample Questions (Q214-Q219):

NEW QUESTION # 214
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes.
You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?

  • A. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
  • B. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
  • C. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
  • D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

Answer: A


NEW QUESTION # 215
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

  • A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
  • B. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
  • C. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
  • D. Use the NOW () function in BigQuery to record the event's time.

Answer: C

Explanation:
Topic 2, 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.


NEW QUESTION # 216
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

  • A. Use a row key of the form >#<sensorid>#<timestamp>.
  • B. Use a row key of the form <timestamp>.
  • C. Use a row key of the form <timestamp>#<sensorid>.
  • D. Use a row key of the form <sensorid>.

Answer: B


NEW QUESTION # 217
You currently use a SQL-based tool to visualize your data stored in BigQuery The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

  • A. Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours. Reference: the parquet files in the data visualization tool.
  • B. Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness parameter to 4 hours and the enable_refresh parameter to true. Reference: the materialized views in the data visualization tool.
  • C. Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference: the materialized views in the data visualization tool.
  • D. Create views for the visualization queries. Reference: the views in the data visualization tool.

Answer: C

Explanation:
To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here's why option C is the best choice:
Materialized Views:
Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.
They can be automatically refreshed to reflect changes in the underlying data.
Incremental Updates:
The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.
This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.
Performance and Maintenance:
By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.
This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.
Steps to Implement:
Create Materialized Views:
Define materialized views for the visualization queries with the necessary configurations CREATE MATERIALIZED VIEW project.dataset.view_name AS SELECT ...
FROM ...
WHERE ...
Enable Incremental Updates:
Ensure that the materialized views are set up to handle incremental updates automatically.
Reference:
Update the data visualization tool to reference the materialized views instead of running the original queries directly.
BigQuery Materialized Views
Optimizing Query Performance


NEW QUESTION # 218
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

  • A. Use a larger set of features
  • B. Increase the regularization parameters
  • C. Reduce the number of training examples
  • D. Decrease the regularization parameters
  • E. Get more training examples
  • F. Use a smaller set of features

Answer: A,D,E


NEW QUESTION # 219
......

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