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100+ Free CA ANZ Data Analytics and Insights Practice Questions

CA Program Elective: Data Analytics and Insights (CACC1509) practice questions are available now; exam metadata is being verified.

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A date field stores values as both '2026-01-15' and '15/01/2026'. Before analysis, which data-preparation task is required?

A
B
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D
to track
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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 Practice Questions

Verified exam format metadata for CA Program Elective: Data Analytics and Insights (CACC1509) is pending. The practice questions above remain available while official exam length, timing, passing score, fee, and administrator details are reviewed.