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Table of Contents
Question
You are a data analyst at a company and are part of a team that involves a data scientist. Once the data scientist has finished sifting through the data to solve a problem, they hand off the data to you. What do you do with this data?
A. See if the data scientist overlooked something when solving a problem.
B. Ensure that the data is accurate by reviewing the work from the data scientist.
C. Create ways to make this data visual to management and outside stakeholders.
D. Create ways to improve the infrastructure to make the data even better next time.
Answer
C. Create ways to make this data visual to management and outside stakeholders.
Explanation
Data analysts transform complex insights into visual stories for stakeholders, while data scientists focus on predictive modeling. Understand how these roles complement each other in GCP’s analytics ecosystem and prepare for your certification exam.
When a data scientist hands off processed data to a data analyst, option C is indeed the correct answer: “Create ways to make this data visual to management and outside stakeholders.” This accurately reflects the core responsibilities of a data analyst in the analytics workflow.
Understanding Data Analyst vs. Data Scientist Roles
Data scientists and data analysts have distinct but complementary roles in the data analytics ecosystem:
Data Scientists
- Focus on predictive modeling and strategic decision-making
- Design and develop machine learning algorithms
- Handle complex, unstructured data sets
- Explore the unknown through advanced statistical techniques
- Create repeatable, reusable code for data processing
Data Analysts
- Process and interpret data to extract valuable insights
- Present findings in easily comprehensible formats
- Use data visualization tools like Tableau, Power BI, and Excel
- Create reports that help stakeholders understand key data insights
- Engage with managers to specify data requirements tailored to business processes
Why Option C is Correct
The data analyst’s primary responsibility after receiving processed data from a data scientist is to translate complex findings into visual representations that stakeholders can understand and act upon. This aligns perfectly with option C.
Data visualization is a critical skill for data analysts because:
- It transforms complex data into accessible insights
- It enables non-technical stakeholders to comprehend findings
- It facilitates data-driven decision-making across the organization
Why Other Options Are Incorrect
Option A: “See if the data scientist overlooked something when solving a problem.”
This suggests the analyst should review or validate the data scientist’s work, which isn’t typically part of their core responsibilities. Data scientists are advanced specialists with deeper expertise in complex data modeling.
Option B: “Ensure that the data is accurate by reviewing the work from the data scientist.”
While data quality is important, reviewing a data scientist’s work for accuracy implies a supervisory role that doesn’t align with the typical relationship between these positions.
Option D: “Create ways to improve the infrastructure to make the data even better next time.”
Infrastructure improvements typically fall under the domain of data engineers or IT specialists, not data analysts whose focus is on interpretation and presentation.
By selecting option C, you correctly identify the data analyst’s primary function in making complex data insights accessible to decision-makers through effective visualization and presentation.
Performing Smart Analytics and AI on Google Cloud Platform skill assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Performing Smart Analytics and AI on Google Cloud Platform exam and earn Performing Smart Analytics and AI on Google Cloud Platform certification.