Professional-Data-Engineer Dump Ready - Exam Questions and Answers

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The Google Certified Professional Data Engineer Exam certification exam is divided into multiple sections, each of which covers a specific area of data engineering. Professional-Data-Engineer Exam is scored on a scale of 1000, with a passing score of 700 or higher. Professional-Data-Engineer exam is computer-based and can be taken at a testing center or online. The cost of the exam is $200, and it is valid for two years.

Google Professional-Data-Engineer certification exam is designed for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. As a Google Certified Professional Data Engineer, you will be recognized as an expert in the field and have the skills to create scalable and efficient data processing systems that leverage Google Cloud technologies. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data architects, solutions architects, and anyone who wants to validate their skills in building and managing data processing systems in the cloud.

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Google Certified Professional Data Engineer Exam Sample Questions (Q323-Q328):

NEW QUESTION # 323
Your company is implementing a data warehouse using BigQuery and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

Answer: A


NEW QUESTION # 324
Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query:

How should you redesign the BigQuery table to support faster access?

Answer: B

Explanation:
To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:
Clustering in BigQuery:
Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.
Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.
Filter Efficiency:
With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.
This directly addresses the performance issue of the dashboard queries that apply filters on these fields.
Steps to Implement:
Redesign the Table:
Create a new table with clustering on countryname and username:
CREATE TABLE project.dataset.new_table
CLUSTER BY countryname, username AS
SELECT * FROM project.dataset.customer_order;
Migrate Data:
Transfer the existing data from the original table to the new clustered table.
Update Queries:
Modify the dashboard queries to reference the new clustered table.
Reference:
BigQuery Clustering Documentation
Optimizing Query Performance


NEW QUESTION # 325
What are two of the benefits of using denormalized data structures in BigQuery?

Answer: B

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table.
Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


NEW QUESTION # 326
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

Answer: A


NEW QUESTION # 327
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Answer: D

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers


NEW QUESTION # 328
......

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