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| Section | Weight | Objectives |
|---|
| Topic 1: Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Optimize pipeline performance and cost
- 2. Build ETL and ELT pipelines
- 3. Design orchestration workflows
- Designing for data ingestion
- 1. Select appropriate storage systems
- 2. Design batch and streaming ingestion solutions
- 3. Design scalable and reliable ingestion architecture
|
| Topic 2: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 2. Implement streaming data systems
- 3. Implement batch processing systems
- Operationalizing systems
- 1. Monitor data pipelines and workloads
- 2. Manage logging and alerting
- 3. Implement fault tolerance and recovery
|
| Topic 3: Managing and optimizing solutions | 20%-25% | - Managing resources and costs
- 1. Control operational costs
- 2. Optimize storage and compute usage
- 3. Tune query and pipeline performance
- Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Design highly available systems
- 3. Scale data workloads efficiently
|
| Topic 4: Ensuring solution quality | 20%-25% | - Security and compliance
- 1. Manage IAM and access control
- 2. Protect sensitive data
- 3. Implement encryption and compliance controls
- Data quality management
- 1. Validate and clean data
- 2. Implement data governance
- 3. Maintain metadata and lineage
|
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Google Data Engineer Sample Questions (Q13-Q18):
NEW QUESTION # 13
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. A processing time trigger
- B. The with Allowed Lateness method
- C. An event time trigger
- D. An hourly watermark
Answer: A
Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers
NEW QUESTION # 14
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?
- A. In the event of an emergency, use the backup copy of the table.
- B. Set the BigQuery dataset to be multi-regional
- C. Set the BigQuery dataset to be regiona
- D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
- E. In the event of an emergency, use a point-in-time snapshot to recover the data.
- F. Set the BigQuery dataset to be regiona
- G. In the event of an emergency, use a point-in-time snapshot to recover the data.
- H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
- I. In the event of an emergency, use the backup copy of the table.
- J. Set the BigQuery dataset to be multi-regional
Answer: E
NEW QUESTION # 15
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. VPN
- B. SOCKS
- C. HTTPS
- D. HTTP
Answer: B
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 # 16
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .
- A. worker node
- B. master node
- C. conditional node
- D. application node
Answer: B
Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 17
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
- A. Denormalize the data as must as possible.
- B. Use BigQuery'ssupport for external data sources to query.
- C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
- E. Preserve the structure of the data as much as possible.
- F. Use BigQuery UPDATE to further reduce the size of the dataset.
Answer: C,D
NEW QUESTION # 18
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