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| Section | Weight | Objectives |
|---|
| Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Supporting model training and serving
- Enabling data analysis
- 1. Implementing data sharing and governance
- 2. Preparing datasets for querying and reporting
- 3. Optimizing query performance
|
| Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
- Operationalizing workloads
- 1. Monitoring and troubleshooting pipelines
- 2. Ensuring reliability and recoverability
- 3. Orchestrating and scheduling jobs
|
| Designing data processing systems | 24% | - Designing data pipelines
- 1. Defining architecture for batch and streaming processing
- 2. Designing for data transformation and enrichment
- 3. Planning for data migration and integration
- Planning data solutions
- 1. Selecting appropriate storage solutions
- 2. Designing for reliability, scalability, and efficiency
- 3. Planning for data security and compliance
|
| Ingesting and processing data | 25% | - Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Implementing ingestion mechanisms
- 3. Handling data quality and consistency
- Transforming data
- 1. Managing schema evolution
- 2. Optimizing transformations for performance
- 3. Applying data processing logic
|
| 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 (Q34-Q39):
NEW QUESTION # 34
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 BigTable
- B. Cloud SQL
- C. Cloud Datastore
- D. BigQuery
Answer: A
NEW QUESTION # 35
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- B. Add a SideInput that returns a Boolean if the element is corrupt.
- C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
Answer: C
NEW QUESTION # 36
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
- A. Cloud SQL
- B. Cloud Bigtable
- C. BigQuery
- D. Cloud Datastore
Answer: C
NEW QUESTION # 37
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the WHERE_PARTITIONTIME pseudo column
- B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- D. Use the TABLE_DATE_RANGE function
Answer: D
Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap
NEW QUESTION # 38
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
- A. Trigger that is a combination of other triggers
- B. Trigger based on element count
- C. Trigger based on time
- D. Trigger based on element size in bytes
Answer: D
Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers
NEW QUESTION # 39
......
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