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| Section | Weight | Objectives |
|---|
| Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Supporting model training and serving
- 2. Feature engineering and data preparation
- Enabling data analysis
- 1. Optimizing query performance
- 2. Implementing data sharing and governance
- 3. Preparing datasets for querying and reporting
|
| Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Optimizing resource usage and costs
- 2. Automating deployment and management
- Operationalizing workloads
- 1. Monitoring and troubleshooting pipelines
- 2. Ensuring reliability and recoverability
- 3. Orchestrating and scheduling jobs
|
| Ingesting and processing data | 25% | - Transforming data
- 1. Managing schema evolution
- 2. Applying data processing logic
- 3. Optimizing transformations for performance
- Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Implementing ingestion mechanisms
- 3. Handling data quality and consistency
|
| Designing data processing systems | 24% | - Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Selecting appropriate storage solutions
- 3. Planning for data security and compliance
- Designing data pipelines
- 1. Designing for data transformation and enrichment
- 2. Planning for data migration and integration
- 3. Defining architecture for batch and streaming processing
|
| Storing and managing data | 20% | - Implementing storage solutions
- 1. Using data storage services appropriately
- 2. Managing data lifecycle and retention
- 3. Designing data warehouses and data lakes
- Optimizing storage performance and cost
- 1. Implementing cost optimization strategies
- 2. Configuring storage for access patterns
|
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Google Data Engineer Sample Questions (Q47-Q52):
NEW QUESTION # 47
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. VPN
- B. HTTPS
- C. SOCKS
- D. HTTP
Answer: C
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 48
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
- A. Keep your row key as long as the field permits
- B. Keep the row keep as an 8 bit integer
- C. Keep your row key reasonably short
- D. Include multiple time series values within the row key
Answer: C
Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys
NEW QUESTION # 49
Which of the following statements is NOT true regarding Bigtable access roles?
- A. You can configure access control only at the project level.
- B. To give a user access to only one table in a project, you must configure access through your application.
- C. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
- D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
Answer: C
Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control
NEW QUESTION # 50
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 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
- B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
- D. 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
Answer: D
NEW QUESTION # 51
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. BigQuery
- B. Cloud Datastore
- C. Cloud Dataflow
- D. Cloud Bigtable
Answer: A
Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive
NEW QUESTION # 52
......
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