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| Section | Weight | Objectives |
|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Automation and repeatability
- 1. Implementing CI/CD for data systems
- 2. Automating deployment and updates
- Resource optimization
- 1. Choosing appropriate compute and storage options
- 2. Cost management and resource allocation
|
| Topic 2: Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
- 1. Model serving and monitoring
- 2. Optimizing model performance and cost
- Preparing data for ML
- 1. Handling structured and unstructured data
- 2. Feature engineering and data preparation
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
- Building data pipelines
- 1. Orchestrating data workflows
- 2. Transforming and cleaning data
- 3. Ingesting data from various sources
|
| Topic 4: Designing data processing systems | 20% | - Designing for regulatory and security requirements
- 1. Ensuring data privacy and compliance
- 2. Implementing access control and data protection
- Designing for business requirements
- 1. Designing for scalability and elasticity
- 2. Designing for reliability and fault tolerance
- 3. Selecting appropriate storage solutions
|
| Topic 5: Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
- 1. Diagnosing performance issues
- 2. Optimizing queries and workloads
- Testing and validating data systems
- 1. Performance and scalability testing
- 2. Data quality validation
|
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Google Certified Professional Data Engineer Exam Sample Questions (Q401-Q406):
NEW QUESTION # 401
You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?
- A. Edge TPUs as sensor devices for storing and transmitting the messages.
- B. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.
- C. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
- D. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
Answer: D
NEW QUESTION # 402
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)
- A. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
- B. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- D. Introduce data compression for each file to increase the rate file of file transfer.
- E. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
Answer: C,E
NEW QUESTION # 403
Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow.
What should you do?
- A. Create a transactional database that monitors the pending messages.
- B. Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.
- C. Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.
- D. Use Pub/Sub exactly-once delivery in your pull subscription.
Answer: D
Explanation:
Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages. References:
* Exactly-once delivery | Cloud Pub/Sub Documentation
* Cloud Pub/Sub Exactly-once Delivery feature is now Generally Available (GA)
NEW QUESTION # 404
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?
- A. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic
- B. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
- C. Use Transfer Appliance to copy the data to Cloud Storage
- D. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
Answer: C
Explanation:
Explanation/Reference:
NEW QUESTION # 405
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?
- A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- B. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- C. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
Answer: B
NEW QUESTION # 406
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