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Test Professional-Data-Engineer Assessment & Professional-Data-Engineer Flexible Learning Mode

With the arrival of the flood of the information age of the 21st century, people are constantly improve their knowledge to adapt to the times. But this is still not enough. In the IT industry, Google's Professional-Data-Engineer exam certification is the essential certification of the IT industry. Because this exam is difficult, through it, you may be subject to international recognition and acceptance, and you will have a bright future and holding high pay attention. TestBraindump has the world's most reliable IT certification training materials, and with it you can achieve your wonderful plans. We guarantee you 100% certified. Candidates who participate in the Google Professional-Data-Engineer Certification Exam, what are you still hesitant？Just do it quickly!

Google Professional-Data-Engineer exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer Exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

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Quiz Google - Professional-Data-Engineer - Google Certified Professional Data Engineer Exam –The Best Test Assessment

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Google Professional Data Engineer exam covers a wide range of topics, including the understanding of the Google Cloud Platform for storing, processing, and analyzing data, designing data processing systems, data modeling, data security, and compliance. Additionally, the exam tests the candidate's knowledge of implementing data pipelines, data transformation and processing, and machine learning models on the Google Cloud Platform. Passing Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge required to design and build data processing systems that meet business requirements and scale efficiently on the Google Cloud Platform.

To prepare for the Google Professional-Data-Engineer Certification Exam, candidates should have hands-on experience with GCP data engineering services, including BigQuery, Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. They should also have experience with data modeling, data warehousing, and machine learning. Google recommends that candidates take the Data Engineering on Google Cloud Platform specialization on Coursera before taking the exam.

Google Certified Professional Data Engineer Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

A. Google Cloud Storage
B. Google BigQuery
C. Cloud Bigtable
D. Google Cloud Datastore

Answer: C

Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp

NEW QUESTION # 25
You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather dat a. You want to avoid storing unnecessary data and minimize costs. What should you do?

A. Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
B. Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.
C. Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to 30 days.
D. Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.

Answer: B

Explanation:
Partitioning a table by ingestion time means that the data is divided into partitions based on the time when the data was loaded into the table. This allows you to delete or archive old data by setting a partition expiration policy. You can specify the number of days to keep the data in each partition, and BigQuery automatically deletes the data when it expires. This way, you can avoid storing unnecessary data and minimize costs.

NEW QUESTION # 26
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Instead of feeding in each feature individually, average their values in batches of 3.
B. Eliminate features that are highly correlated to the output labels.
C. Combine highly co-dependent features into one representative feature.
D. Remove the features that have null values for more than 50% of the training records.

Answer: C

NEW QUESTION # 27
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log dat a. How should you set up the log data transfer into Google Cloud?

A. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
B. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Answer: C

Explanation:
storage bucket as a final destination.

NEW QUESTION # 28
You currently have transactional data stored on-premises in a PostgreSQL database. To modernize your data environment, you want to run transactional workloads and support analytics needs with a single database. You need to move to Google Cloud without changing database management systems, and minimize cost and complexity. What should you do?

A. Migrate to BigQuery to optimize analytics.
B. Migrate your PostgreSQL database to Cloud SQL for PostgreSQL.
C. Migrate your workloads to AlloyDB for PostgreSQL.
D. Migrate and modernize your database with Cloud Spanner.

Answer: C

Explanation:
The key requirements are:
On-premises PostgreSQL database.
Run transactional workloads AND support analytics needs with a single database.
Move to Google Cloud without changing database management systems (i.e., remain PostgreSQL- compatible).
Minimize cost and complexity.
AlloyDB for PostgreSQL (Option A) is the best fit for these requirements.
PostgreSQL-Compatible: AlloyDB is fully PostgreSQL-compatible, meaning minimal to no application changes are required ("without changing database management systems").
Transactional and Analytical Workloads: AlloyDB is designed to handle demanding transactional workloads while also providing significantly faster analytical query performance compared to standard PostgreSQL. It achieves this through its intelligent, database-optimized storage layer and columnar engine integration. This addresses the "single database" for both needs.
Cost and Complexity: As a managed service, it reduces operational complexity. Its performance benefits for both OLTP and OLAP can lead to better cost-efficiency by handling mixed workloads effectively on a single system.
Let's analyze why other options are less suitable:
B (Migrate to BigQuery): BigQuery is an analytical data warehouse, not designed for transactional workloads.
This violates the "single database" for both types of workloads and "without changing database management systems" (as BigQuery is not PostgreSQL).
C (Migrate to Cloud Spanner): Cloud Spanner is a globally distributed, horizontally scalable relational database. While excellent for high-availability transactional workloads, it has its own SQL dialect (ANSI
2011 with extensions, not fully PostgreSQL wire-compatible without tools like PGAdapter, which adds complexity) and a different architecture. This would involve more significant changes than moving to a PostgreSQL-compatible system. The requirement was "without changing database management systems." D (Migrate to Cloud SQL for PostgreSQL): Cloud SQL for PostgreSQL is a fully managed PostgreSQL service. It's excellent for transactional workloads and simpler analytical queries. However, for more demanding analytical needs on the same database instance, AlloyDB is specifically optimized to provide superior performance due to its architectural enhancements (like the columnar engine). If the analytical needs are significant, AlloyDB offers a better converged experience. While Cloud SQL is PostgreSQL-compatible, AlloyDB is positioned for superior performance on mixed workloads.
Reference:
Google Cloud Documentation: AlloyDB for PostgreSQL > Overview. "AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service for your most demandingtransactional and analytical workloads... AlloyDB offers full PostgreSQL compatibility, so you can migrate your existing PostgreSQL applications with no code changes." Google Cloud Documentation: AlloyDB for PostgreSQL > Key benefits. Highlights include "Industry-leading performance: ...up to 100x faster analytical queries than standard PostgreSQL." and "Support for transactional and analytical workloads: AlloyDB is designed to efficiently handle both transactional and analytical queries, allowing you to use a single database for a wide range of applications."

NEW QUESTION # 29
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