2026 Pass-Sure Associate GCP-DE Level Exam | Data Engineer 100% Free Practice Test Online

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| Section | Weight | Objectives |
|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
- Operationalizing workloads
- 1. Orchestrating and scheduling jobs
- 2. Monitoring and troubleshooting pipelines
- 3. Ensuring reliability and recoverability
|
| Topic 2: 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. Preparing datasets for querying and reporting
- 3. Implementing data sharing and governance
|
| Topic 3: 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. Handling data quality and consistency
- 2. Developing batch and streaming workflows
- 3. Implementing ingestion mechanisms
|
| Topic 4: Storing and managing data | 20% | - Optimizing storage performance and cost
- 1. Implementing cost optimization strategies
- 2. Configuring storage for access patterns
- Implementing storage solutions
- 1. Using data storage services appropriately
- 2. Managing data lifecycle and retention
- 3. Designing data warehouses and data lakes
|
| Topic 5: Designing data processing systems | 24% | - Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Planning for data security and compliance
- 3. Selecting appropriate storage solutions
- Designing data pipelines
- 1. Designing for data transformation and enrichment
- 2. Defining architecture for batch and streaming processing
- 3. Planning for data migration and integration
|
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Google Data Engineer Sample Questions (Q26-Q31):
NEW QUESTION # 26
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. Cloud Datastore
- B. BigQuery
- C. Cloud Dataflow
- D. Cloud Bigtable
Answer: B
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 # 27
Which is not a valid reason for poor Cloud Bigtable performance?
- A. The workload isn't appropriate for Cloud Bigtable.
- B. The table's schema is not designed correctly.
- C. There are issues with the network connection.
- D. The Cloud Bigtable cluster has too many nodes.
Answer: D
Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance
NEW QUESTION # 28
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: dateColumn data: device_id, data_point
- B. Rowkey: date#device_idColumn data: data_point
- C. Rowkey: date#data_pointColumn data: device_id
- D. Rowkey: device_idColumn data: date, data_point
- E. Rowkey: data_pointColumn data: device_id, date
Answer: E
NEW QUESTION # 29
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
- A. Use at least 300 GB of data.
- B. Do not use a production instance.
- C. Before you test, run a heavy pre-test for several minutes.
- D. Run your test for at least 10 minutes.
Answer: B
Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance
NEW QUESTION # 30
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud SQL for storag
- B. Use Cloud Spanner for storag
- C. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud Dataflow to transform data to support query patterns.
- E. Add secondary indexes to support query patterns.
- F. Use Cloud SQL for storag
- G. Use Cloud Spanner for storag
- H. Add secondary indexes to support query patterns.
Answer: D
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 31
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