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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing and Analytics | 20-30% | - Aggregate and summarize data - Use BigQuery and SQL for analytics - Build and maintain data pipelines - Apply statistical methods for analysis - Query and analyze datasets |
| Topic 2: Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Identify data quality issues - Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Ingest and acquire data |
| Topic 3: Data Visualization and Insights | 20-30% | - Choose appropriate visualization types - Create dashboards and reports - Build visualizations using Looker Studio - Interpret and communicate findings - Present data insights to stakeholders |
| Topic 4: Data-Driven Decision Making | 10-20% | - Define success metrics - Translate business requirements into data solutions - Assess data quality and completeness - Identify stakeholders and requirements |
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
Correct Answer: C 🗳️
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
Correct Answer: A 🗳️
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
Correct Answer: A 🗳️
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
Correct Answer: A 🗳️
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?
Correct Answer: C 🗳️
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