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HOT Latest Braindumps Professional-Data-Engineer Book 100% Pass | Trustable Exam Google Certified Professional Data Engineer Exam Reviews Pass for sure

HOT Latest Braindumps Professional-Data-Engineer Book 100% Pass | Trustable Exam Google Certified Professional Data Engineer Exam Reviews Pass for sure

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Google Professional-Data-Engineer Exam is intended for professionals who work with data engineering, data integration, or data analysis. Professional-Data-Engineer exam tests the candidate's knowledge and understanding of Google Cloud Platform tools and services, including BigQuery, Cloud Dataflow, Cloud Pub/Sub, Cloud Storage, and more. Professional-Data-Engineer exam consists of multiple-choice questions and practical scenarios that test the candidate's ability to apply their knowledge and skills to real-world problems. Passing the exam and obtaining the certification demonstrates the individual's proficiency in designing and implementing scalable and reliable data processing systems using Google Cloud Platform technologies.

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Google Certified Professional Data Engineer Exam Sample Questions (Q110-Q115):

NEW QUESTION # 110
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 number of workers
  • B. The disk size per worker
  • C. The maximum number of workers
  • D. The zone

Answer: D


NEW QUESTION # 111
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

  • A. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
  • B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
  • C. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
  • D. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Answer: D


NEW QUESTION # 112
Your company receives both batch- and stream-based event data. You want to process the data using
Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data
can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is
late or out of order?

  • A. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define
    the logic for lagged data.
  • B. Use watermarks and timestamps to capture the lagged data.
  • C. Set sliding windows to capture all the lagged data.
  • D. Set a single global window to capture all the data.

Answer: C


NEW QUESTION # 113
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

  • A. Use the LIMIT keyword to reduce the number of rows returned.
  • B. Recreate the table with a partitioning column and clustering column.
  • C. Create a separate table for each ID.
  • D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.

Answer: A


NEW QUESTION # 114
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?

  • A. Create a sale3_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
  • B. Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.
  • C. Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.
  • D. Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.

Answer: A

Explanation:
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes.
Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load.
Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECT id, SUM(line_items.quantity * line_items.price) AS total_amount
FROM sales_transaction
GROUP BY id;
AI-generated code. Review and use carefully. More info on FAQ.
References:
* Use nested and repeated fields
* BigQuery explained: Working with joins, nested & repeated data
* Arrays in BigQuery - How to improve query performance and optimise storage


NEW QUESTION # 115
......

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