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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q392-Q397):

NEW QUESTION # 392
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
B. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
D. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Answer: C

Explanation:
As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis.

NEW QUESTION # 393
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

A. Host a visualization tool on a VM on Google Compute Engine.
B. Grant the user access to Google Cloud Shell.
C. Run a local version of Jupiter on the laptop.
D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Answer: B

NEW QUESTION # 394
You are collecting IoT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data, filtered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Partition table data by create_date, cluster table data by location_id, and device_version.
B. Cluster table data by create_date, location_id, and device_version.
C. Partition table data by create_date, location_id, and device_version.
D. Cluster table data by create_date, partition by location_id, and device_version.

Answer: A

NEW QUESTION # 395
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Access historical data by using time travel in BigQuery.
B. Migrate your data to multi-region BigQuery buckets.
C. Create a BigQuery table snapshot on a daily basis.
D. Export the data from BigQuery into a new table that excludes the corrupted data.

Answer: A

Explanation:
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
Reference:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps

NEW QUESTION # 396
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

A. Only streaming
B. BigQuery cannot be used as a sink
C. Both batch and streaming
D. Only batch

Answer: C

Explanation:
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io

NEW QUESTION # 397
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