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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	- Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads - Security and governance 1. IAM and access control in GCP 2. Data governance and compliance
Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection - ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores
Designing data processing systems	22%	- Batch and streaming data processing design 1. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs - Data architecture and storage design 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) 2. Designing scalable and cost-effective data models
Building and operationalizing data processing systems	24%	- Data processing and transformation 1. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)

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最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q71-Q76):

問題 #71
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

A. 1. Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all zones' data assets.
B. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all lakes' data assets.
C. 1 Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own zone's data assets.
D. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own lake's data assets.

答案：C

解題說明：
To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
Data Mesh Architecture:
Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
Dataplex Lakes and Zones:
Lakes in Dataplex are logical containers for managing data at scale, and zones are subdivisions within lakes for organizing data based on domains, teams, or other criteria.
Domain and Team Management:
By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
Create Lakes and Zones:
Create separate lakes in Dataplex for each team (analytics and data science).
Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
Attach BigQuery Datasets:
Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
Decentralized Management:
Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference:
Dataplex Documentation
BigQuery Documentation
Data Mesh Principles

問題 #72
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?

A. Train a classifier with deep neural networks, because neural networks would always beat SVMs
B. Perform hyperparameter tuning
C. Deploy the model and measure the real-world AUC; it's always higher because of generalization
D. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

答案：D

解題說明：
Explanation/Reference:

問題 #73
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

A. 'bigquery-public-data.noaa_gsod.gsod'*
B. bigquery-public-data.noaa_gsod.gsod*
C. 'bigquery-public-data.noaa_gsod.gsod*`
D. 'bigquery-public-data.noaa_gsod.gsod'

答案：B

問題 #74
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

A. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
B. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
C. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
D. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.
Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

答案：B

問題 #75
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Option A
B. Option C
C. Option D
D. Option B.

答案：A

問題 #76
......

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