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100+ Free CA ANZ Data Analytics and Insights Practice Questions

Prepare for the CA Program Elective: Data Analytics and Insights (CACC1509) exam with instant access — no signup required.

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Key Facts: CA ANZ Data Analytics and Insights Exam

7 weeks

Elective Subject Length

CA ANZ Data Analytics and Insights page

Power BI

Required Tool (Desktop)

CA ANZ Data Analytics and Insights page

~95 hrs

Study and Preparation

CA ANZ

100

Free Practice Questions

OpenExamPrep

Report + presentation

Assessment Format

CA ANZ Data Analytics and Insights page

GradDipCA

Part of CA Program

CA ANZ CA Program

CA ANZ Data Analytics and Insights (CACC1509) is a 7-week online elective in the GradDipCA. Candidates use data-analysis techniques and Microsoft Power BI Desktop to frame, solve, and communicate business problems, and are assessed through a written report and a recorded presentation rather than a fixed multiple-choice exam. CA ANZ indicates around 95 hours of study. Because the subject is partly practical, this free bank focuses on the strongly testable conceptual body: analytics types and technique selection, data preparation and quality, data visualisation and Power BI, and problem framing, communication, ethics, and governance.

Sample CA ANZ Data Analytics and Insights Practice Questions

Try these sample questions to test your CA ANZ Data Analytics and Insights exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1A management accountant produces a monthly dashboard showing total revenue, units sold, and average selling price for the prior period. Which type of analytics does this dashboard primarily represent?
A.Descriptive analytics
B.Predictive analytics
C.Prescriptive analytics
D.Diagnostic analytics
Explanation: Descriptive analytics summarises what has already happened by aggregating historical data into metrics, trends, and totals. A dashboard reporting past revenue, volumes, and prices is the textbook example of descriptive analytics.
2A finance team notices a sudden drop in gross margin and drills into product mix, discount codes, and regional data to find the cause. Which analytics maturity stage does this investigation represent?
A.Descriptive analytics
B.Diagnostic analytics
C.Predictive analytics
D.Prescriptive analytics
Explanation: Diagnostic analytics answers 'why did it happen?' by drilling down, segmenting, and finding root causes. Investigating product mix, discounts, and region to explain a margin drop is diagnostic work.
3Which type of analytics uses historical data and statistical models to estimate the likelihood of future outcomes, such as which customers are most likely to churn?
A.Descriptive analytics
B.Diagnostic analytics
C.Predictive analytics
D.Prescriptive analytics
Explanation: Predictive analytics applies statistical and machine-learning models to historical data to forecast what is likely to happen, such as churn probability or demand. It produces estimates of future events rather than recommendations.
4A logistics optimiser recommends the specific delivery routes and inventory levels that minimise total cost given forecasted demand and capacity constraints. Which analytics type is this?
A.Descriptive analytics
B.Predictive analytics
C.Diagnostic analytics
D.Prescriptive analytics
Explanation: Prescriptive analytics recommends the optimal course of action, often using optimisation and simulation, given predictions and constraints. Recommending specific routes and inventory levels to minimise cost is prescriptive.
5In the commonly used analytics maturity model, the four types are typically ordered from least to most advanced as:
A.Descriptive, diagnostic, predictive, prescriptive
B.Predictive, descriptive, diagnostic, prescriptive
C.Diagnostic, descriptive, prescriptive, predictive
D.Prescriptive, predictive, diagnostic, descriptive
Explanation: The analytics maturity ladder runs descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what should we do). Each stage builds on the prior and adds business value and complexity.
6An auditor wants to test 100% of journal entries for unusual round-dollar postings made after hours. Which analytics approach best fits this objective?
A.Predictive modelling of future entries
B.Full-population descriptive testing with anomaly rules
C.Prescriptive optimisation of the posting schedule
D.Clustering customers by revenue band
Explanation: Testing the entire population against defined rules (round amounts, after-hours timestamps) is descriptive, rule-based anomaly detection applied to full data rather than a sample. Data analytics lets auditors examine 100% of transactions efficiently.
7Which technique groups observations into segments based on similarity without using a predefined target variable?
A.Linear regression
B.Logistic regression
C.Cluster analysis
D.Time-series forecasting
Explanation: Cluster analysis is an unsupervised technique that groups records by similarity without a labelled target. It is widely used for customer segmentation and pattern discovery.
8A model predicts whether a loan applicant will default (yes or no). Which supervised technique is most appropriate for this binary classification?
A.Moving-average smoothing
B.Linear regression
C.K-means clustering
D.Logistic regression
Explanation: Logistic regression models the probability of a binary outcome (default / no default) and is a standard classification technique. Its output is bounded between 0 and 1, suitable for yes/no predictions.
9In a simple linear regression of sales on advertising spend, the coefficient on advertising spend is 4.2. What does this coefficient indicate?
A.Each one-unit increase in advertising spend is associated with a 4.2-unit increase in sales, holding other factors constant
B.Sales are 4.2 times advertising spend
C.Advertising spend explains 4.2% of sales variation
D.The correlation between sales and advertising is 4.2
Explanation: A regression slope coefficient is the estimated change in the dependent variable for a one-unit change in the independent variable. A coefficient of 4.2 means sales rise by 4.2 units for each additional unit of advertising spend.
10A regression model reports an R-squared of 0.85. What is the best interpretation?
A.The model predicts with 85% accuracy on new data
B.85% of the variation in the dependent variable is explained by the model
C.The slope coefficient equals 0.85
D.There is an 85% chance the relationship is causal
Explanation: R-squared measures the proportion of variance in the dependent variable explained by the independent variables in the sample. An R-squared of 0.85 means the model accounts for 85% of that variation.

About the CA ANZ Data Analytics and Insights Exam

Data Analytics and Insights (CACC1509) is a 7-week CA Program elective in which candidates identify, analyse, solve, and communicate business problems using data-analysis techniques and modern business intelligence tools, requiring Microsoft Power BI Desktop and assessed through a written report and recorded presentation.

Assessment

Performance-based assessment

Time Limit

7-week online elective subject

Passing Score

Set by CA ANZ via subject assessment; no public fixed MCQ pass mark is published

Exam Fee

Set by CA ANZ per CA Program subject enrolment; no single fixed standalone elective price is published on the public course page (Chartered Accountants Australia and New Zealand (CA ANZ))

CA ANZ Data Analytics and Insights Exam Content Outline

~35%

Analytics Types and Technique Selection

Descriptive, diagnostic, predictive, and prescriptive analytics, the analytics maturity ladder, regression and classification, clustering, time-series, association rules, and choosing techniques to fit the business question.

~20%

Data Preparation and Quality

Structured and unstructured data, ETL, cleaning, standardisation, handling missing values and outliers, joins and aggregation, Power Query, and data-quality dimensions such as completeness, validity, uniqueness, and consistency.

~20%

Data Visualisation and Power BI

Visualisation principles, chart selection, data-ink ratio, dashboards, DAX measures and calculated columns, slicers, relationships, drill-down, accessibility, and honest, non-misleading visual design.

~25%

Problem Framing and Communicating Insights

Framing business problems, scoping, hypotheses, KPIs, validating results, tailoring messages to audiences, data storytelling, communicating uncertainty and limitations, and applying analytics ethics and governance to influence stakeholder decisions.

How to Pass the CA ANZ Data Analytics and Insights Exam

What You Need to Know

  • Passing score: Set by CA ANZ via subject assessment; no public fixed MCQ pass mark is published
  • Assessment: Performance-based assessment
  • Time limit: 7-week online elective subject
  • Exam fee: Set by CA ANZ per CA Program subject enrolment; no single fixed standalone elective price is published on the public course page

Keys to Passing

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

CA ANZ Data Analytics and Insights Study Tips from Top Performers

1Learn the four analytics types (descriptive, diagnostic, predictive, prescriptive) and practise matching each to a business question.
2Get hands-on early in Power BI Desktop, since the subject's assessment is practical and Power BI-based.
3Drill data-quality dimensions (completeness, validity, uniqueness, consistency) and ETL and cleaning steps until they are second nature.
4Practise choosing the right chart for the message and avoiding misleading visuals such as truncated axes.
5Rehearse communicating insights concisely to a non-technical audience, leading with the implication and stating assumptions and uncertainty.
6Always connect analysis back to the framed business problem and the decision it must inform.

Frequently Asked Questions

What is CA ANZ Data Analytics and Insights (CACC1509)?

It is a 7-week elective subject in the CA Program Graduate Diploma of Chartered Accounting. Candidates identify, analyse, solve, and communicate business problems using data-analysis techniques and modern business intelligence tools, with a strong hands-on Power BI component.

Do I need Microsoft Power BI for this CA ANZ elective?

Yes. CA ANZ states that candidates need access to the Desktop version of Microsoft Power BI to complete the subject, which is used for the practical data-preparation, analysis, and visualisation work.

How is the Data Analytics and Insights subject assessed?

The subject uses practical, assessment-based components, including a written report and a recorded presentation, rather than a single fixed multiple-choice exam. Candidates transform data into actionable insights and communicate them.

How long is the CA ANZ Data Analytics and Insights subject?

It is delivered as a 7-week online elective through the My Capability Learn platform. CA ANZ indicates around 95 hours of study and preparation for the subject.

Is this practice bank a substitute for the official subject?

No. Because the subject is partly practical and Power BI-based, this free bank focuses on the conceptual analytics body that is strongly testable, such as analytics types, data preparation, visualisation principles, communication, and ethics. It complements, not replaces, the official course.

What topics does the conceptual content cover?

Key areas are analytics types and technique selection, data preparation and quality, data visualisation and Power BI, and problem framing and communicating insights, including analytics ethics and governance.