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| Section | Weight | Objectives |
|---|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Scale data workloads efficiently
- 2. Implement disaster recovery strategies
- 3. Design highly available systems
- Managing resources and costs
- 1. Tune query and pipeline performance
- 2. Optimize storage and compute usage
- 3. Control operational costs
|
| 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. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
|
| Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Implement batch processing systems
- 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 3. Implement streaming data systems
- Operationalizing systems
- 1. Implement fault tolerance and recovery
- 2. Manage logging and alerting
- 3. Monitor data pipelines and workloads
|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
- 1. Design batch and streaming ingestion solutions
- 2. Select appropriate storage systems
- 3. Design scalable and reliable ingestion architecture
- Designing data pipelines
- 1. Build ETL and ELT pipelines
- 2. Design orchestration workflows
- 3. Optimize pipeline performance and cost
|
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Google Data Engineer Sample Questions (Q23-Q28):
NEW QUESTION # 23
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
- A. after
- B. before
- C. only if
- D. once
Answer: B
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 24
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
- A. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- C. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
- D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
- E. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
Answer: A
NEW QUESTION # 25
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud SQL
- B. Cloud BigTable
- C. Cloud Datastore
- D. BigQuery
Answer: B
NEW QUESTION # 26
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase the size of the Google Persistent Disk on your server.
- B. Increase your network bandwidth from your datacenter to GCP.
- C. Increase the CPU size on your server.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
Answer: B
NEW QUESTION # 27
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?
- A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
- B. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
- C. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
- E. Merge the transformed tables together with a SQL query
- F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
Answer: F
NEW QUESTION # 28
......
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