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The Google Professional-Data-Engineer exam is intended for data engineers, data analysts, and other professionals who work with large data sets and need to design and implement scalable, reliable, and efficient data processing systems. It is also suitable for IT professionals who are responsible for managing data pipelines and ensuring the security, privacy, and compliance of data on Google Cloud Platform.

The Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate’s ability to design and implement data processing systems, as well as manage data pipelines, data storage, and data security on Google Cloud Platform. Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, which require candidates to demonstrate their knowledge of various topics such as data modeling, data warehousing, data visualization, and machine learning.

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The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing, storage, analysis, transformation, and visualization on Google Cloud Platform. Candidates are expected to have a deep understanding of Google Cloud Platform services and tools, as well as the ability to design and implement scalable, reliable, and efficient data processing systems that meet business requirements. Google Certified Professional Data Engineer Exam certification exam is rigorous and challenging, requiring candidates to demonstrate their ability to apply their knowledge and skills to real-world scenarios. Successful candidates will be able to demonstrate their proficiency in designing and building data processing systems on Google Cloud Platform and will be recognized as experts in this field.

Google Certified Professional Data Engineer Exam Sample Questions (Q186-Q191):

NEW QUESTION # 186
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

A. Use Dataflow to move files into Cloud Storage.
B. Use Storage Transfer Service to move files into Cloud Storage.
C. Use BigQuery Data Transfer Service to move files into BigQuery.
D. Use Cloud Data Fusion to move files into Cloud Storage.

Answer: D

Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
Cloud Data Fusion:
Cloud Data Fusion is a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
Custom Encryption Keys:
Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
Centralizing Data:
Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
Set Up Cloud Data Fusion:
Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
Create ETL Pipelines:
Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
Run and Monitor Pipelines:
Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference:
Cloud Data Fusion Documentation
Using Customer-Managed Encryption Keys (CMEK)

NEW QUESTION # 187
When a Cloud Bigtable node fails, ____ is lost.

A. all data
B. no data
C. the last transaction
D. the time dimension

Answer: B

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost

NEW QUESTION # 188
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

A. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
B. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
C. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
D. Check the dashboard application to see if it is not displaying correctly.

Answer: A

Explanation:
Explanation:

NEW QUESTION # 189
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

A. Use Data Fusion to build and execute ETL pipelines
B. Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.
C. Use Dataform to build, manage, and schedule SQL pipelines.
D. Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.

Answer: C

Explanation:
To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:
ELT Development Technique:
ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.
BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.
Dataform:
Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.
It provides tools for managing SQL as code, including version control and collaborative development.
Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.
Intuitive Coding Environment:
Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.
It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.
Managing SQL as Code:
Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.
This allows for better collaboration, code reviews, and version tracking.
Reference:
Dataform Documentation
BigQuery Documentation
Managing ELT Pipelines with Dataform

NEW QUESTION # 190
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

A. export the data from the existing instance and import the data into a new instance
B. the selection is final and you must resume using the same storage type
C. create a third instance and sync the data from the two storage types via batch jobs
D. run parallel instances where one is HDD and the other is SDD

Answer: A

Explanation:
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write
a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.

NEW QUESTION # 191
......

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