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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for reliability and fault tolerance 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
Topic 2: Operationalizing machine learning models	20%	- 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
Topic 3: Maintaining and automating data workloads	18%	- Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Topic 4: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Topic 5: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes

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Google Certified Professional Data Engineer Exam Sample Questions (Q334-Q339):

NEW QUESTION # 334
You are building a system to process financial transactions. The system must handle a high throughput of concurrent user operations and each operation requires low-latency reads and writes to individual records. You need to identify a storage solution that guarantees ACID compliance for the processed transactions. You want a Google Cloud managed service. What should you do?

A. Select Spanner.
B. Select Cloud Storage.
C. Select Bigtable.
D. Select BigQuery.

Answer: A

Explanation:
Cloud Spanner is a fully managed, globally consistent database that provides ACID transactions, low-latency reads and writes, and can handle high throughput for concurrent operations, making it suitable for financial transaction processing.

NEW QUESTION # 335
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

A. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.
B. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance
C. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.
D. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

Answer: A

Explanation:
The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files.Reference:https://cloud.google.com/memorystore/docs/redis/import-export- overview

NEW QUESTION # 336
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A. Put the data into Google Cloud Storage.
B. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
C. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
D. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Answer: A

NEW QUESTION # 337
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

A. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
B. Export the information to Cloud Stackdriver, and set up an Alerting policy
C. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
D. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Answer: B

Explanation:
Monitoring does not only provide you with access to Dataflow-related metrics, but also lets you to create alerting policies and dashboards so you can chart time series of metrics and choose to be notified when these metrics reach specified values.

NEW QUESTION # 338
Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.

A. details
B. id
C. value
D. null

Answer: C

NEW QUESTION # 339
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