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| Section | Weight | Objectives |
|---|
| Topic 1: 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. Planning for data security and compliance
- 2. Selecting appropriate storage solutions
- 3. Designing for reliability, scalability, and efficiency
|
| Topic 2: Ingesting and processing data | 25% | - Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Handling data quality and consistency
- 3. Implementing ingestion mechanisms
- Transforming data
- 1. Optimizing transformations for performance
- 2. Applying data processing logic
- 3. Managing schema evolution
|
| Topic 3: 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. Configuring storage for access patterns
- 2. Implementing cost optimization strategies
|
| Topic 4: Preparing data for analysis and machine learning | 13% | - Enabling data analysis
- 1. Preparing datasets for querying and reporting
- 2. Optimizing query performance
- 3. Implementing data sharing and governance
- Preparing data for ML
- 1. Supporting model training and serving
- 2. Feature engineering and data preparation
|
| Topic 5: Maintaining and automating data workloads | 18% | - Operationalizing workloads
- 1. Orchestrating and scheduling jobs
- 2. Ensuring reliability and recoverability
- 3. Monitoring and troubleshooting pipelines
- Automation and optimization
- 1. Optimizing resource usage and costs
- 2. Automating deployment and management
|
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Google Data Engineer Sample Questions (Q17-Q22):
NEW QUESTION # 17
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 Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- C. Add a SideInput that returns a Boolean if the element is corrupt.
- D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
Answer: D
NEW QUESTION # 18
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. An event time trigger
- B. An hourly watermark
- C. The with Allowed Lateness method
- D. A processing time trigger
Answer: D
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 # 19
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Create a table on Cloud Spanner, and insert and delete rows with the job information
- B. Create an API using App Engine to receive and send messages to the applications
- C. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- D. Create a table on Cloud SQL, and insert and delete rows with the job information
Answer: C
NEW QUESTION # 20
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 Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
- B. 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
- C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- 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 # 21
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)
- A. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- B. Introduce data compression for each file to increase the rate file of file transfer.
- C. Assemble 1,000 files into a tape archive (TAR) fil
- D. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
- E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- F. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
Answer: D,E
NEW QUESTION # 22
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