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| Section | Weight | Objectives |
|---|---|---|
| Data Management and Governance | 25% | - Data quality and maintenance
|
| Data Preparation and Ingestion | 30% | - Storage solutions selection
|
| Data Pipeline Orchestration | 18% | - Data transformation concepts
|
| Data Analysis and Presentation | 27% | - Data exploration and analysis
|
Question 1
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?
A. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
Question 2
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?
A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
C. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
Question 3
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?
A. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
B. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
C. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D. Apply a filter to only show products with a positive profit margin.
Question 4
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?
A. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
B. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
C. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
D. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
Question 5
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?
A. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
B. Create a custom quota for each analyst in BigQuery.
C. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: A | Question 5 Answer: C |
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