Professional-Data-Engineer최신업데이트버전인증시험자료 - Professional-Data-Engineer유효한덤프

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
Ensuring solution quality and reliability17%- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
Designing data processing systems20%- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for scalability and elasticity
- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection
Operationalizing machine learning models20%- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation

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최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q87-Q92):

질문 # 87
You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

정답：B


질문 # 88
Your organization uses a multi-cloud data storage strategy, storing data in Cloud Storage, and data in Amazon Web Services' (AWS) S3 storage buckets. All data resides in US regions. You want to query up-to-date data by using BigQuery. regardless of which cloud the data is stored in. You need to allow users to query the tables from BigQuery without giving direct access to the data in the storage buckets What should you do?

정답：B

설명：
BigQuery Omni enables you to run BigQuery analytics directly on data stored in AWS S3 buckets without having to move or copy the data. This provides several benefits:
Reduced Data Movement Costs: Eliminates the need to egress data from AWS, potentially saving significant costs.
Real-Time Analytics: Allows you to query data in AWS S3 in real-time, providing up-to-date insights.
Simplified Architecture: Reduces the complexity of managing data pipelines and ETL processes.
Here's a breakdown of the steps involved in using BigQuery Omni:
Set up a BigQuery Omni connection: This involves configuring the connection between your Google Cloud project and your AWS S3 bucket. This connection establishes the secure link for BigQuery to access the data in AWS S3.
Create external tables: BigQuery external tables are a way to query data residing in external storage systems, such as AWS S3, without having to import the data into BigQuery. This enables you to directly query the data in its original location.
Query the data using BigQuery: Once the external tables are created, you can use standard SQL queries to analyze the data stored in both Cloud Storage and AWS S3, just as if it were native BigQuery data.
Why other options are not suitable:
Option A: BigLake tables are designed for storing large volumes of structured data within BigQuery itself, not for directly querying data in external storage systems.
Option C and D: While the Storage Transfer Service is useful for moving data between cloud providers, it introduces unnecessary data movement and latency if the goal is to simply query the data in its original location.
Key Points:
BigQuery Omni extends BigQuery's capabilities to analyze data stored in other cloud providers, such as AWS.
External tables provide a way to query data in external storage systems without having to import it into BigQuery.
By leveraging BigQuery Omni and external tables, you can efficiently and cost-effectively query data stored in multiple cloud environments using a single tool, BigQuery.


질문 # 89
You manage your company's BigQuery data warehouse. You need to implement a solution that enables the data science team to modify data for experiments without affecting the original tables, while minimizing additional storage costs. What should you do?

정답：D

설명：
BigQuery Table Clones are specifically designed for the use case where you need a writable copy of a table that is storage-efficient.
* Writable and Independent: Unlike views (which are read-only) or snapshots (which are read-only until restored), a table clone is a lightweight, writable copy. The data science team can perform DML operations (INSERT, UPDATE, DELETE) on the clone without those changes reflecting in the production base table.
* Minimal Storage Costs: Table clones use a copy-on-write mechanism. Initially, the clone consumes zero additional storage because it points to the same underlying physical data blocks as the base table.
You are only billed for the data that differs between the clone and the base table (i.e., new or modified rows in the clone).
* Correcting other options:
* A (Authorized Views): Views do not allow the data science team to modify or "experiment" with the data; they only allow querying of the existing production data.
* B (Snapshots): Snapshots are read-only. To modify them, they must be restored into a standard table, at which point you are charged for the full storage of that restored table, failing the
"minimize storage costs" requirement.
* D (Full Copies): This is the most expensive option as it duplicates all physical data, leading to significantly higher storage costs.
Reference: Google Cloud Documentation on BigQuery Table Clones:
"A table clone is a lightweight, writable copy of another table (called the base table). You are only charged for storage of data in the table clone that differs from the base table, so initially there is no storage cost for a table clone... Common use cases include: Creating sandboxes for users to generate their own analytics and data manipulations, without physically copying all of the production data." (Source: Introduction to table clones)


질문 # 90
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

정답：C

설명：
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.


질문 # 91
You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-cade option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

정답：A

설명：
https://cloud.google.com/dialogflow/es/docs/how/detect-intent-tts


질문 # 92
......

Google인증Professional-Data-Engineer시험을 패스하여 자격증을 취득한다면 여러분의 미래에 많은 도움이 될 것입니다.Google인증Professional-Data-Engineer시험자격증은 it업계에서도 아주 인지도가 높고 또한 알아주는 시험이며 자격증 하나로도 취직은 문제없다고 볼만큼 가치가 있는 자격증이죠.Google인증Professional-Data-Engineer시험은 여러분이 it지식테스트시험입니다.

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