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| Section | Weight | Objectives |
|---|
| Topic 1: Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Design orchestration workflows
- 2. Optimize pipeline performance and cost
- 3. Build ETL and ELT pipelines
- Designing for data ingestion
- 1. Design scalable and reliable ingestion architecture
- 2. Select appropriate storage systems
- 3. Design batch and streaming ingestion solutions
|
| Topic 2: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Design highly available systems
- 2. Scale data workloads efficiently
- 3. Implement disaster recovery strategies
- Managing resources and costs
- 1. Tune query and pipeline performance
- 2. Optimize storage and compute usage
- 3. Control operational costs
|
| Topic 3: Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
- 1. Manage logging and alerting
- 2. Monitor data pipelines and workloads
- 3. Implement fault tolerance and recovery
- Building data processing systems
- 1. Implement streaming data systems
- 2. Implement batch processing systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
|
| Topic 4: Ensuring solution quality | 20%-25% | - Data quality management
- 1. Validate and clean data
- 2. Maintain metadata and lineage
- 3. Implement data governance
- Security and compliance
- 1. Implement encryption and compliance controls
- 2. Manage IAM and access control
- 3. Protect sensitive data
|
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Google Data Engineer Sample Questions (Q40-Q45):
NEW QUESTION # 40
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Locate the bottleneck and adjust cluster resources.
- B. Configure the job to use non-default Compute Engine machine types when needed.
- C. Resize the number of worker nodes in your cluster via the command line.
- D. Monitor the total execution time for a sampling of job
- E. Use the diagnose command to generate an operational output archiv
- F. Monitor the job system lag with Stackdrive
- G. Use Cloud Dataflow to run your transformation
- H. Use Cloud Dataproc to run your transformation
- I. Use Cloud Dataflow to run your transformation
- J. Use the default autoscaling setting for worker instances.
- K. Monitor CPU utilization for the cluste
- L. Use Cloud Dataproc to run your transformation
Answer: K
NEW QUESTION # 41
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Execute to Google Cloud Storage; execute to Google Cloud Logging
- B. Execute to Google Cloud Storage; write to Google Cloud Logging
- C. Read and write to Google Cloud Storage; write to Google Cloud Logging
- D. Write to Google Cloud Storage; read to Google Cloud Logging
Answer: C
Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes
NEW QUESTION # 42
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- D. Create a cron schedule in Cloud Dataprep.
Answer: C
NEW QUESTION # 43
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. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
- 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. Assemble 1,000 files into a tape archive (TAR) fil
- E. Introduce data compression for each file to increase the rate file of file transfer.
- F. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
Answer: C,F
NEW QUESTION # 44
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Train your own image recognition model leveraging transfer learning techniques.
- B. Use Cloud Vision API by providing custom labels as recognition hints.
- C. Use Cloud Vision AutoML with the existing dataset.
- D. Use Cloud Vision AutoML, but reduce your dataset twice.
Answer: C
NEW QUESTION # 45
......
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