Pre-Assessment Study
Which project is considered a data analytics project? Building a predictive model to forecast stock prices for a financial services company Developing a recommendation system to suggest new products to customers based on their past purchases Designing a database schema to store customer information for a retail store Creating a dashboard to visualize sales data and monitor inventory levels for a grocery store chain
Creating a dashboard to visualize sales data and monitor inventory levels for a grocery store chain
Who offers suggestions on ideas to test as the team formulates hypotheses during the discovery phase of a data analytics project? · Data scientists · Data visualization specialists · Project managers · Marketing experts
Data scientists
Which tools are commonly used for communicating results in data analytics projects? Predictive modeling software and programming languages Text editors and spreadsheet software Database management systems and data warehouses Data visualization tools and presentation software
Data visualization tools and presentation software
Which type of data analytics project aims to determine why something happened in the past? Diagnostic Descriptive Predictive Prescriptive
Descriptive
What are the different types of data analytics projects? Regression analysis, time series analysis, text analytics, and network analysis Descriptive, diagnostic, predictive, and prescriptive analytics Data collection, data cleaning, data transformation, and data visualization Data warehousing, data mining, data visualization, and business intelligence
Descriptive, diagnostic, predictive, and prescriptive analytics
What is a primary responsibility of a data engineer? Designing and implementing data storage solutions Designing and developing data visualizations for stakeholders Analyzing and interpreting data to inform business decisions Developing predictive models using machine learning algorithms
Designing and implementing data storage solutions
What is the role of a business intelligence analyst? Overseeing data governance and compliance Designing and maintaining data visualizations and dashboards Conducting statistical analysis and machine learning modeling Developing and implementing data processing pipelines
Designing and maintaining data visualizations and dashboards
What is a primary responsibility of a machine learning engineer? Designing and developing data visualizations for stakeholders Analyzing and interpreting data to inform business decisions Developing predictive models using machine learning algorithms Designing and implementing data storage solutions
Developing predictive models using machine learning algorithms
In which phase of the Data Analytics Life Cycle does the data science team investigate the problem, develop context and understanding, learn about available data sources, and formulate initial hypotheses? Model planning Data preparation Discovery Model execution
Discovery
Which activities should the data analytics team perform during the model execution phase of this project? Deploying the model and measuring its return on investment Grouping categorical variables and standardizing numeric values Generating training and test sets and refining models to enhance performance Creating data visualizations and capturing essential predictors
Generating training and test sets and refining models to enhance performance
A popular travel booking platform receives a large volume of web traffic, GPS location data, and user-generated content from various sources. The data analytics team is preparing this data for analysis to better understand customer behavior and preferences. Which tool would be most suitable for preparing this data? Hadoop Tableau Microsoft Excel Power BI
Hadoop
A data analyst plans to explore possible indicators of fraud in bank transactions. The analyst is considering different tools that can be used to collect the data needed. Which question should the analyst consider when identifying the tool to use? · Are all relevant variables present? · What is the research question for the project? · What is the timeline for the project? · What is the format and structure of the data?
What is the format and structure of the data?
Why is quality control/assurance crucial for data engineers in a data analytics project? It ensures that the data is analyzed in a timely manner. It ensures that the data is accurate and reliable. It ensures that the data is accessible to all stakeholders. It ensures that the data is stored in a secure location.
It ensures that the data is accurate and reliable.
How does the communication of results tie to the operationalize phase of data analytics · It helps identify the relevant data sources. · It implements data-driven insights into business functions. · It ensures the accuracy of the data analysis. · It enables the development of a data model.
It implements data-driven insights into business functions.
Which tool is commonly used during the model planning phase? Data Wrangler OpenRefine KNIME Hadoop
KNIME
A healthcare company wants to predict which patients are at risk of developing a certain medical condition. Which model is commonly used for this type of analysis? Decision tree Association rules Logistic regression K-means clustering
Logistic regression
A data analyst working for a digital marketing agency wants to analyze customer data to identify factors that are most strongly associated with customer churn. The analyst has access to a database of customer information, which includes data such as age, gender, location, income, purchasing behavior, engagement with the agency's services, and customer satisfaction ratings. Which data analytic technique should be used to identify factors that are strongly associated with customer churn for the ag
Logistic regression analysis
What is a skill required of a data engineer? · Creating data visualizations · Maintaining databases · Writing programs that perform data analysis · Training machine learning models
Maintaining databases
During a data analytics project, which phase focuses on developing training and test datasets, refining models, and assessing the validity and predictive power of the models? Data preparation Model planning Operationalize Model execution
Model execution
In the data analytics process, which phase focuses on identifying candidate models for clustering, classifying, or finding relationships and ensuring analytical techniques align with business objectives? Model planning Data preparation Data transformation Discovery
Model planning
What should analysts do with the findings discovered during the operationalize phase of a data analytics project? Evaluate the project's success Create technical specifications Assess project risks and return on investment (ROI) Modify reports and dashboards
Modify reports and dashboards
Which tool is commonly used for data preparation? OpenRefine SAS Enterprise Miner Tableau R
OpenRefine
What role does a project manager play within a data analytics project? Collect and analyze data Oversee the project team and ensure the project is completed on time and within budget Interpret the project results and make recommendations for future projects Provide funding and resources for the project
Oversee the project team and ensure the project is completed on time and within budget
Which activities should be the focus of the model planning phase? Transforming data to bring information to the surface Cleaning and conditioning data for analysis Partitioning the data into training, validation, and test sets Visualizing and exploring data patterns
Partitioning the data into training, validation, and test sets
What is the purpose of the communicate results phase in a data analytics project? Evaluating the project's financial and technical results Creating and refining analytical models Preparing and managing data for analysis Presenting findings and outcomes to stakeholders
Presenting findings and outcomes to stakeholders
Which activity should the data analytics team focus on during the communicate results phase Analyzing the financial impact of the project on the company's revenue and customer retention Performing data cleaning and transforming raw data into usable formats Presenting key findings to stakeholders and evaluating the project's success Building and testing different predictive models for customer churn correct
Presenting key findings to stakeholders and evaluating the project's success
Which groups make up the key stakeholders in a data analytics project? Project team members and senior management Shareholders and investors Competitors and regulatory agencies Manufacturers and suppliers
Project team members and senior management
What role do stakeholders play in the project cycle? Execute the project tasks Define the project scope and objectives Provide guidance and feedback throughout the project Create the project plan and schedule
Provide guidance and feedback throughout the project
Which stakeholder should conduct literature reviews for a data analytics project? Project sponsor Researcher Database administrator End user
Researcher
Which tool is suitable for a data analytics team to use during the model execution phase of a project? KNIME SAS Enterprise Miner Microsoft Excel Tableau
SAS Enterprise Miner
Which activity is performed during the model planning phase of a data analysis project? · Building the final predictive model · Generating synthetic data for model training · Conducting hypothesis testing on the modeling data · Selecting relevant features for modeling
Selecting relevant features for modeling
Which sequence of steps should you follow during the data preparation phase? Set up sandbox, extract and transform data, condition data, explore visually Generate visuals, modify data, analyze patterns, cooperate with IT department Obtain data, store data, create charts, finalize report Formulate hypothesis, gather data, examine findings, conclude analysis
Set up sandbox, extract and transform data, condition data, explore visually
A company recently completed a data analytics project to identify the most energy-efficient products to add to the catalog. The project team comprised business users, project sponsors, analysts, data scientists, data engineers, and database administrators. Now, the team needs to share their findings with various stakeholders. What should the data scientists, data engineer, and database administrator do to share their findings? Share code and provide implementation details Assess the benefits a
Share code and provide implementation details
A data analyst works at an e-commerce company that wants to understand its customer churn rate. Their manager has tasked them with conducting a data analytics project to identify customers at risk of churn and offer these customers targeted promotions to retain their business. What is the most suitable form of deliverable in this scenario? Monthly sales reports Supply chain improvements Updated website design Lists of at-risk customers
Supply chain improvements
A retail company wants to improve its sales and customer satisfaction by analyzing customer data. The company hired a data analytics team, which has access to the company's customer database, including transaction records, demographic information, and customer feedback. The data analytics team will work closely with the marketing and IT departments to create actionable insights for the company. The team has three months to complete the project, and the company's budget allows purchasing addition
The customer database
What is data science? The study of how computers interact with human language A field that involves creating data visualizations to provide insights The process of creating computer programs to automate tasks The practice of using statistical methods to extract insights from data
The practice of using statistical methods to extract insights from data
What is data analytics? The process of storing data in a secure location for future use The process of collecting data from various sources The process of analyzing data to extract insights The process of encrypting data to keep it secure
The process of analyzing data to extract insights
Why is a project sponsor a key stakeholder in a data analytics project? They are the primary users of the project's outputs. They provide funding for the project. They ensure that the project aligns with business goals and objectives. They are responsible for implementing the project.
They ensure that the project aligns with business goals and objectives.
Why are financial operation stakeholders important in a data analytics project? They are responsible for data cleaning and migration within a project. They help design and implement data analytics projects. They provide financial resources for the project. They interpret data and provide insights to improve financial performance.
They interpret data and provide insights to improve financial performance.
A clothing store tracks sales data for 5 months. The number of items sold, the average price per item, and the total revenue generated monthly. Required to analyze their sales data to identify any trends or patterns and improve their sales strategy. Which data analytic technique would be most appropriate to identify any trends or patterns in the clothing store's sales data? · Time series analysis · Cluster analysis · Regression analysis · Factor analysis
Time series analysis
What is the role and function of a decision scientist within an organization? To oversee the company's human resources and ensure employee satisfaction To manage the company's finances and ensure profitability To analyze data and provide insights to support informed decision-making To develop marketing strategies and increase sales revenue
To analyze data and provide insights to support informed decision-making
What component of a data analytics project is typically completed by a data analyst? To design and implement machine learning algorithms To collect and store data for the organization To clean and preprocess data to prepare it for analysis To make decisions based on the insights derived from data analysis
To clean and preprocess data to prepare it for analysis
What is the primary purpose of the data preparation phase in a data analytics project? To visualize and explore data patterns To evaluate the performance of models To clean, normalize, and transform data To build and refine predictive models
To clean, normalize, and transform data
What is the function of a data scientist in an organization? To oversee data governance and compliance To conduct statistical analysis and machine learning modeling To design and maintain data visualizations and dashboards To work independently to analyze data and make decisions based on their findings
To conduct statistical analysis and machine learning modeling
What is the purpose of communicating data analytics results to stakeholders? · To demonstrate the value and impact of data analytics on business outcomes · To share technical details and methodologies used in the analysis · To validate the accuracy and reliability of the data used in the analysis · To persuade stakeholders to adopt new data analytics tools and techniques
To demonstrate the value and impact of data analytics on business outcomes
What is the main purpose of the model execution phase in a data analytics project? To develop datasets, refine models, and assess validity To select appropriate models based on project goals To deploy the model and calculate its financial impact To clean, transform, and aggregate data for analysis
To develop datasets, refine models, and assess validity
What is the primary purpose of the operationalize phase in a data analytics project? To develop and train various data models To explore data and partition it into training, validation, and test sets To prepare and clean the data for analysis To pilot the model, refine it, and fully deploy it
To pilot the model, refine it, and fully deploy it
A data analyst works at an e-commerce company that wants to understand its customer churn rate. Their manager has tasked them with conducting a data analytics project to identify customers at risk of churn and offer these customers targeted promotions to retain their business. What is the primary purpose of the data analytics project's results in this scenario? To predict customer churn risk To identify customer preferences To compare the company's churn rate to industry benchmarks To opti
To predict customer churn risk
What is the primary purpose of the discovery phase in the data science process? To understand the business problem and develop initial hypotheses To clean and preprocess the data for analysis To evaluate and optimize data-driven predictive models To develop interactive visualizations for stakeholder presentations
To understand the business problem and develop initial hypotheses
Which data migration skill is necessary for database administrators? Transferring data between different systems or formats Ensuring that the database remains secure Developing and implementing database software Troubleshooting network issues within the system
Transferring data between different systems or formats
Which task is typically performed to handle outliers during the data preparation phase · Normalization · Data transformation · Truncating extreme values · Missing data input
Truncating extreme values
Which data sources would be most relevant for analyzing factors affecting patient satisfaction in a healthcare company? Printing press run records, noise levels, and census data Web log data, call-center records, and survey responses Credit card charge records, telephone call detail records, and point-of-sale data Warranty claims, weather data, and economic data
Web log data, call-center records, and survey responses
What are the necessary skills for partners in a data analytics project? Cloud infrastructure management and automation Business domain knowledge and communication Machine learning algorithm development Data visualization and dashboard development
Business domain knowledge and communication
What is a primary responsibility of a data analyst? Conducting statistical analysis to identify patterns and trends Developing predictive models using machine learning algorithms Designing and implementing data storage solutions Developing data visualizations for stakeholders
Conducting statistical analysis to identify patterns and trends
What does a data analyst do in a data analytics project? Oversees data governance and data quality assurance Designs and develops databases and data pipelines Focuses on building machine learning models Conducts exploratory data analysis to identify trends and patterns
Conducts exploratory data analysis to identify trends and patterns
Which phase of a data analytics project involves articulating findings and outcomes for stakeholders while considering caveats, assumptions, and limitations? Operationalize Model development Communicate results Data preparation
Communicate results
Which information tool is a possible source of data in a data analytics project? Corporate information system Marketing slogans Consumer perception survey questions Company logo designs
Corporate information system
Which job skill is necessary for a researcher in a data analytics project? Identifying business needs and requirements Analyzing and interpreting data to inform questions Designing and implementing data storage solutions Ensuring data privacy and security
Analyzing and interpreting data to inform questions
What should business users and project sponsors do with their findings during the operationalize phase of a data analytics project? Assess benefits, implications, and business impact Produce detailed reports and visuals Evaluate project completion and goals Develop and refine data models
Assess benefits, implications, and business impact
Which task is commonly performed to identify and address data quality issues during the data preparation phase? · Conducting data profiling · Performing data deduplification · Developing data visualization · Executing data integration
Conducting data profiling
A DA working for retail co. analyzes customer purchasing behavior, identifies segment of high-value customers who churn. The analyst needs to communicate the results to cust. svc. dpt. to operationalize the info and reduce churn. How does the communication of results tie to the operationalize phase of data analytics? · By implementing personalized outreach to customer · By conducting further data analysis and exploration · By refining data collection processes · By training customer service
By implementing personalized outreach to customers
How does a data analyst interact with stakeholders during a data analytics project? By delegating tasks to stakeholders By providing technical details of data analysis methods By presenting data analysis results in an easily understandable format By making decisions on behalf of stakeholders
By presenting data analysis results in an easily understandable format
How do stakeholders interact with data analytics projects? By providing funding for the project By providing input throughout the project lifecycle By providing consultations at the start of the project By providing finances to complete data visualizations
By providing consultations at the start of the project
Which task is the data analyst responsible for within a data analysis project? Conducting statistical analyses and generating reports Collecting, cleaning, and loading customer data into a data warehouse Developing and implementing software applications Creating the project's overall goals and objectives
Conducting statistical analyses and generating reports
A company in the renewable energy industry is working on a data analytics project to identify which areas are more likely to adopt solar power. The data science team needs to gather relevant data sources for this project. Which data sources are most relevant for a renewable energy company looking to identify areas more likely to adopt solar power? Point-of-sale data, credit card charge records, and telephone call detail records Medical insurance claims data, survey response data, and warranty
Census and economic data, hourly weather readings, and demographic data
What do data analytics teams do in the operationalize phase of a data analytics project? Explore data, create model sets, and partition them into training, validation, and test sets Translate business problems into data mining problems and locate appropriate data Communicate project benefits, set up the pilot project, and deploy in production Apply data transformations to fix problems with data and surface information correct
Communicate project benefits, set up the pilot project, and deploy in production
How is data science different from data analytics? Data science focuses more on data visualization, while data analytics focuses on data cleaning and preprocessing. Data science focuses more on tracking experimental data, and data analytics is based on statistical methods and hypotheses. Data science focuses on developing new algorithms and models, while data analytics focuses on using existing models to analyze data. Data science involves creating new algorithms, while data analytics uses e
Data science focuses on developing new algorithms and models, while data analytics focuses on using existing models to analyze data.
Which stakeholder extracts and transforms data during the discovery phase? · Data engineer · Data scientist · Database administrator · Business intelligence analyst
Data Engineer
Who offers suggestions on ideas to test as the team formulates hypotheses during the discovery phase of a data analytics project? · Data engineer · Data scientist · Database administrator · Business intelligence analyst
Data Scientist
Which comparison describes the difference between data analytics and data science? Data science involves analyzing data from structured sources, while data analytics involves analyzing data from unstructured sources. Data analytics is the process of analyzing data to extract insights, while data science involves building and testing models to make predictions. Data analytics focuses on descriptive analysis, while data science focuses on prescriptive analysis. Data analytics focuses on statis
Data analytics is the process of analyzing data to extract insights, while data science involves building and testing models to make predictions.
Which phase of the data analytics lifecycle involves cleaning data, normalizing datasets, and performing transformations? Data modeling Data exploration Data preparation Data evaluation
Data preparation
What is the difference between exploratory and confirmatory data analytics projects? Exploratory projects involve testing hypotheses and finding patterns in data, while confirmatory projects involve verifying existing hypotheses. Exploratory projects involve analyzing data that is already structured, while confirmatory projects involve analyzing unstructured data. Exploratory projects involve analyzing large datasets, while confirmatory projects involve analyzing smaller datasets. Explorator
Exploratory projects involve testing hypotheses and finding patterns in data, while confirmatory projects involve verifying existing hypotheses.
What is the primary purpose of the model planning phase in the data analytics process? Cleaning and conditioning data for analysis Assessing resources and framing the business problem Transforming data to bring information to the surface Identifying methods and aligning techniques with objectives
Identifying methods and aligning techniques with objectives
An online retail company wants to use data analytics to improve customer satisfaction and increase sales. The company has collected data on customer behavior, purchase history, and customer support interactions. Which outcome is most appropriate for the online retail company's data analytics project? Identifying the number of unique customers who visited the website in the past month Increasing customer satisfaction and sales through targeted recommendations and improved customer support Comp
Increasing customer satisfaction and sales through targeted recommendations and improved customer support
A retail company wants to improve its sales and customer satisfaction by analyzing customer data. The company hired a data analytics team, which has access to the company's customer database, including transaction records, demographic information, and customer feedback. The data analytics team will work closely with the marketing and IT departments to create actionable insights for the company. The team has three months to complete the project, and the company's budget allows purchasing addition
Insufficient time for comprehensive data analysis
Which question of interest is appropriate for a data analytics project to increase a store's sales? What are the store's best-selling products? Should the store expand to a new location? How can the store's social media presence be improved? Which customer segments will most likely respond to a marketing campaign?
Which customer segments will most likely respond to a marketing campaign?
