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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing machine learning models	26%	- ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow) - Data processing and transformation 1. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design
Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
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

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Google Certified Professional Data Engineer Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
You are building a new data pipeline to share data between two different types of applications:
jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create an API using App Engine to receive and send messages to the applications
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: B

Explanation:
Pubsub is used to transmit data in real time and scale automatically.

NEW QUESTION # 28
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Create an anonymized sample of the data for the consultant to work with in a different project.
B. Create a service account and allow the consultant to log on with it.
C. Grant the consultant the Viewer role on the project.
D. Grant the consultant the Cloud Dataflow Developer role on the project.

Answer: D

Explanation:
A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs.
https://cloud.google.com/iam/docs/understanding-service-accounts

NEW QUESTION # 29
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

A. Change the storage class of the Hive partitioned data objects from Coldline to Standard.
B. Create an individual external table for each Hive partition by using a common table name prefix.
Use wildcard table queries to reference the partitioned data.
C. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.
D. Upgrade the external table to a BigLake table. Enable metadata caching for the table.

Answer: D

NEW QUESTION # 30
An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

A. Create and share an authorized view that provides the aggregate results.
B. Create and share a new dataset and table that contains the aggregate results.
C. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
D. Create and share a new dataset and view that provides the aggregate results.

Answer: C

Explanation:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION # 31
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?

A. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
B. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
C. Move your data to Cloud Storage. Run your jobs on Dataproc.
D. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Answer: C

Explanation:
Dataproc's Compatibility with Apache Spark: Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake: Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead: By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes: This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A . Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances: This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C . Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach: While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D . Rewrite your jobs in Apache Beam. Run your jobs in Dataflow: Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.

NEW QUESTION # 32
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