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| Section | Weight | Objectives |
|---|
| Topic 1: Ensuring solution quality | 20%-25% | - Security and compliance
- 1. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
- Data quality management
- 1. Implement data governance
- 2. Validate and clean data
- 3. Maintain metadata and lineage
|
| Topic 2: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Implement streaming data systems
- 2. Implement batch processing systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
- 1. Implement fault tolerance and recovery
- 2. Monitor data pipelines and workloads
- 3. Manage logging and alerting
|
| Topic 3: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Scale data workloads efficiently
- 3. Design highly available systems
- Managing resources and costs
- 1. Control operational costs
- 2. Optimize storage and compute usage
- 3. Tune query and pipeline performance
|
| Topic 4: Designing data processing systems | 22%-27% | - Designing for data ingestion
- 1. Select appropriate storage systems
- 2. Design scalable and reliable ingestion architecture
- 3. Design batch and streaming ingestion solutions
- Designing data pipelines
- 1. Design orchestration workflows
- 2. Build ETL and ELT pipelines
- 3. Optimize pipeline performance and cost
|
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Google Data Engineer Sample Questions (Q14-Q19):
NEW QUESTION # 14
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. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
- B. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
- D. 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
- E. Merge the transformed tables together with a SQL query
- F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
Answer: A
NEW QUESTION # 15
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
- A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
- B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
- C. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
- D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
Answer: A
NEW QUESTION # 16
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. BigQuery
- B. Cloud Datastore
- C. Cloud SQL
- D. Cloud Bigtable
Answer: C
NEW QUESTION # 17
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?
- A. Cloud Datastore and Cloud Bigtable
- B. BigQuery and Cloud Bigtable
- C. BigQuery and Cloud Storage
- D. Cloud Bigtable and Cloud SQL
Answer: B
NEW QUESTION # 18
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Load the data every 30 minutes into a new partitioned table in BigQuery.
- B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- C. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- D. Store the data in Google Cloud Datastor
- E. Store the data in a file in a regional Google Cloud Storage bucke
- F. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
Answer: A
NEW QUESTION # 19
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The web-based Data Engineer (GCP-DE) practice exam is accessible from any major OS, including Mac OS X, Linux, Android, Windows, or iOS. These Google GCP-DE exam questions are browser-based, so there's no need to install anything on your computer. Chrome, IE, Firefox, and Opera all support this Google GCP-DE web-based practice exam. You can take this Data Engineer (GCP-DE) practice exam without plugins and software installation.
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