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Professional-Data-Engineer考題，Professional-Data-Engineer考題資源

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>> Professional-Data-Engineer考題 <<

Professional-Data-Engineer考題資源，最新Professional-Data-Engineer考證

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最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q151-Q156):

問題 #151
You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
* Data engineers, which require lull data lake access
* Analytic users, which require access to curated data
You need to assign access rights to these two groups. What should you do?

A. 1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake.2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.
B. 1. Grant the bigquery.dataownex role on BigQuery datasets and the storage.objectcreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.
C. 1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake.2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.
D. 1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectviewer role on Cloud Storage buckets to data engineers.2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

答案：A

解題說明：
When designing a data mesh on Google Cloud using Dataplex to manage data in BigQuery and Cloud Storage, it is essential to simplify data asset permissions while ensuring that each user group has the appropriate access levels. Here's why option A is the best choice:
Data Engineer Group:
Data engineers require full access to the data lake to manage and operate data assets comprehensively.
Granting the dataplex.dataOwner role to the data engineer group on the customer data lake ensures they have the necessary permissions to create, modify, and delete data assets within the lake.
Analytic User Group:
Analytic users need access to curated data but do not require full control over all data assets. Granting the dataplex.dataReader role to the analytic user group on the customer curated zone provides read-only access to the curated data, enabling them to analyze the data without the ability to modify or delete it.
Steps to Implement:
Grant Data Engineer Permissions:
Assign the dataplex.dataOwner role to the data engineer group on the customer data lake to ensure full access and management capabilities.
Grant Analytic User Permissions:
Assign the dataplex.dataReader role to the analytic user group on the customer curated zone to provide read- only access to curated data.
Reference Links:
Dataplex IAM Roles and Permissions
Managing Access in Dataplex

問題 #152
Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

A. Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
B. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
C. Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.
D. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.

答案：D

解題說明：
Here's a detailed breakdown of why this solution is optimal and why others fall short:
Why Option C is the Best Solution:
Kafka Connect Bridge: This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.
Pub/Sub as a Buffer: Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.
Custom Dataflow Pipeline: Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.
Minimal Latency: By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.
Why Other Options Are Not Ideal:
Option A: Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.
Option B: While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.
Option D: Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.
Additional Considerations:
Schema Management: Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.
Monitoring: Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.
By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high-throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.

問題 #153
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

A. Clear your browser history for the past hour then reload the tab showing the virtualizations.
B. Disable caching by editing the report settings.
C. Refresh your browser tab showing the visualizations.
D. Disable caching in BigQuery by editing table details.

答案：B

問題 #154
You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

A. Reduce the size of the training dataset, and decrease the number of input features.
B. Increase the size of the training dataset, and decrease the number of input features.
C. Reduce the size of the training dataset, and increase the number of input features.
D. Increase the size of the training dataset, and increase the number of input features.

答案：B

解題說明：
https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estim

問題 #155
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

A. Reduce the query frequency to once every hour until the database comes back online.
B. Retry the query every second until it comes back online to minimize staleness of data.
C. Retry the query with exponential backoff, up to a cap of 15 minutes.
D. Issue a command to restart the database servers.

答案：C

解題說明：
https://cloud.google.com/sql/docs/mysql/manage-connections

問題 #156
......

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