DA0-002 Multiple Select — 40 Questions

Question 1

Which TWO of the following are best practices for designing an accessible data visualization? (Choose 2.)

Accepted Answer

Add text labels or patterns to differentiate elements. Option A is correct because adding text labels or patterns (e.g., hatching, shapes, or direct data labels) provides a non-color-dependent way to distinguish data series, which is essential for users with color vision deficiencies or when charts are printed in grayscale. Option E is correct because using colorblind-friendly palettes (e.g., Okabe-Ito or ColorBrewer's colorblind-safe schemes) ensures that the chosen colors remain distinguishable for people with deuteranopia, protanopia, or tritanopia, satisfying WCAG 1.4.1 (Use of Color). Option B is incorrect because relying solely on color to convey information fails accessibility guidelines and excludes users who cannot perceive certain color differences. Option C is incorrect because 3D effects distort data perception, reduce readability, and add visual clutter without improving accessibility. Option D is incorrect because animated transitions can trigger motion sensitivity issues and are not a recognized accessibility best practice for data visualization.

Answer

Rely solely on color to convey information

Answer

Use 3D effects to make charts visually appealing

Answer

Include animated transitions between views

Question 2

A data analyst at a subscription media company is asked to determine whether a new recommendation engine increased average watch time. The analyst plans a two-sample t-test comparing a randomly assigned control group against a randomly assigned treatment group, with watch time recorded in minutes per user per week. Which TWO conditions must hold for the pooled-variance two-sample t-test to be statistically valid here? (Choose two.)

Accepted Answer

The population variances must be equal, or the sample sizes must be large enough for the test's robustness.. The pooled two-sample t-test rests on independent observations and an assumption about variance equality that is relaxed by large, balanced samples. Random assignment to control and treatment satisfies independence, and checking variance similarity or group balance safeguards the test's nominal error rate. Sample-size thresholds and measurement-scale restrictions are not assumptions of this procedure, so they do not belong in the analyst's validation checklist.

Answer

Both groups must contain at least 30,000 users to satisfy the central limit theorem.

Answer

Watch time must be measured on a ratio scale with a true zero and no upper bound.

Answer

The control and treatment groups must have identical means before the experiment begins.

Question 3

Which THREE of the following are properties of ratio data? (Choose THREE.)

Accepted Answer

Supports multiplication and division. Ratio data supports multiplication and division because it has a true, meaningful zero point that indicates the absence of the measured attribute. This allows ratios to be computed (e.g., one value is twice another), which is a defining property of ratio scales in measurement theory.

Answer

Data can be categorized into groups

Answer

Allows negative values

Question 4

A company is designing a data pipeline to process streaming data from social media feeds. Which THREE of the following are characteristics of streaming data? (Select THREE).

Accepted Answer

Data is unbounded and infinite. Streaming data is inherently unbounded and infinite because social media feeds generate a continuous, never-ending flow of events. Unlike batch data, there is no natural end to the stream; new tweets, posts, or interactions arrive constantly, making the dataset theoretically infinite in size.

Answer

Data is processed in micro-batches

Answer

Data is stored permanently before processing

Question 5

A data analyst is preparing a report on customer satisfaction scores. To comply with GDPR, which THREE actions must be taken? (Select THREE.)

Accepted Answer

Ensure aggregates do not identify individuals. Option B is correct because GDPR's data minimization and purpose-limitation principles require that aggregated statistics used for reporting must not allow re-identification of any individual, so ensuring aggregates do not identify individuals protects data subjects' privacy. Option D is correct because anonymizing personally identifiable information (PII) such as names, email addresses, and account numbers removes the personal data from scope of GDPR processing, satisfying the regulation's requirement to protect identifiable data. Option E is correct because GDPR Article 5(1)(e) mandates storage limitation, meaning the analyst must establish defined data retention periods for the report data rather than keeping it longer than necessary. Option A is incorrect because retaining data indefinitely violates the GDPR storage-limitation principle. Option C is incorrect because including customer names for context unnecessarily introduces identifiable personal data, contradicting data minimization and the anonymization requirement.

Answer

Retain data indefinitely for analysis

Answer

Include customer names for context

Question 6

An e-commerce company wants to analyze sales performance across product categories. The dataset includes transaction amounts and a column 'category' with values (Electronics, Clothing, Home). The analyst decides to use stratified sampling to ensure proportional representation. Which THREE steps are required to implement this? (Select THREE).

Accepted Answer

Calculate the proportion of each category in the population. Stratified sampling requires first partitioning the population into homogeneous subgroups, so option C is correct: dividing the dataset into three strata based on the 'category' column (Electronics, Clothing, Home) creates the strata. Next, option A is correct because the analyst must calculate each category's proportion of the total population to determine how many samples each stratum should contribute for proportional representation. Option B is also correct because, after determining stratum sizes, a random sample must be drawn from each stratum with size proportional to its population proportion, which is the defining allocation step of proportional stratified sampling. Option D is incorrect because selecting every 10th transaction describes systematic sampling, not stratified sampling, and it ignores the category strata. Option E is incorrect because merging all categories into one group and performing simple random sampling removes the stratification and does not guarantee proportional representation across categories.

Answer

Select every 10th transaction from the entire dataset

Answer

Combine all categories into a single group and perform simple random sampling

Question 7

A data analyst needs to perform a stratified random sample of a customer database. Which TWO steps are essential for this sampling method? (Select two.)

Accepted Answer

Randomly select a proportional number from each stratum. Option D is correct because stratified random sampling begins by partitioning the population into homogeneous subgroups called strata, typically based on a shared characteristic such as age, region, or customer tier, so that each subgroup is internally similar. Option C is correct because, after the strata are formed, the analyst must draw a random sample from each stratum, usually in a number proportional to that stratum's size in the population, ensuring the sample reflects the population's structure. Option A is incorrect because simple random sampling on the whole population ignores the strata and is a different sampling method. Option B is incorrect because randomly selecting entire clusters describes cluster sampling, not stratified sampling. Option E is incorrect because selecting every nth customer is systematic sampling, which does not require dividing the population into strata.

Answer

Use simple random sampling on the whole population

Answer

Randomly select entire clusters of customers

Answer

Select every nth customer from a list

Question 8

A data team must implement a data retention policy to reduce storage costs while meeting legal requirements. Which TWO actions best achieve this?

Accepted Answer

Set data retention limits with automated deletion. Option A is correct because setting data retention limits with automated deletion enforces the legal retention policy by removing data once its required retention period expires, directly reducing stored volume and storage costs. Option E is correct because archiving historical data to tape or cloud archive tiers moves infrequently accessed data to much cheaper storage media while still preserving it for legal and compliance requirements. Together, these two actions address both cost reduction and legal retention. Option B (data compression) reduces the size of stored data but does not enforce retention or remove data that has exceeded its legal retention period. Option C (increasing primary storage capacity) raises costs rather than reducing them and does nothing to meet retention requirements. Option D (data deduplication) eliminates redundant copies but does not implement a retention policy or remove expired data.

Answer

Use data compression

Answer

Increase primary storage capacity

Answer

Implement data deduplication

Question 9

A university database stores student information in a normalized schema. The 'students' table has a primary key 'student_id'. The 'enrollments' table has a foreign key 'student_id' referencing 'students'. Which two of the following are true about primary and foreign keys? (Select TWO)

Accepted Answer

A foreign key ensures referential integrity between tables. A foreign key enforces referential integrity by ensuring that every value in the foreign key column of the 'enrollments' table matches a valid primary key value in the 'students' table. This prevents orphaned records and maintains consistency across related tables in a normalized relational database.

Answer

A foreign key must have the same name as the primary key it references

Answer

A foreign key can reference a column that is not a primary key

Answer

A table can have multiple primary keys

Question 10

A data analyst is performing data acquisition from multiple source files. Which TWO data profiling tasks should the analyst complete before loading the data into the target system?

Accepted Answer

Verify data types and formats. Option B (Verify data types and formats) is correct because data profiling must confirm that each source column's actual data type (e.g., integer, string, date) and format (e.g., YYYY-MM-DD, decimal separators) match what the target system expects, preventing load failures or silent corruption. Option E (Identify missing values and nulls) is correct because profiling must detect NULLs, empty strings, and placeholder values so the analyst can decide on imputation, defaulting, or rejection rules before loading. Option A (Create a dashboard for stakeholders) is a downstream reporting activity, not a pre-load profiling task. Option C (Build a linear regression model) is predictive analytics performed after data is cleansed and loaded, not profiling. Option D (Perform cluster analysis) is an unsupervised modeling technique also done post-load, so it does not belong in pre-load data profiling.

Answer

Create a dashboard for stakeholders

Answer

Build a linear regression model

Answer

Perform cluster analysis

Question 11

A data analyst is presenting a complex statistical analysis to a group of data scientists. The audience is highly knowledgeable. Which TWO approaches are most appropriate? (Choose two.)

Accepted Answer

Include technical details and methodology. Option C is correct because a highly knowledgeable audience of data scientists can evaluate and trust the analysis when the presenter includes the technical details and methodology, such as the statistical tests, model choices, and parameter settings used. Option D is correct because presenting the assumptions and limitations of the analysis is essential for a technical audience to properly interpret the results, assess validity, and understand the boundaries of the conclusions. Options A, B, and E are not appropriate: hiding uncertainty undermines scientific integrity, basic visualizations like pie charts are inadequate for complex statistical findings, and oversimplifying would frustrate or mislead an expert audience rather than clarify the analysis.

Answer

Avoid mentioning uncertainty to maintain confidence

Answer

Use basic visualizations like pie charts

Answer

Simplify the findings to avoid confusion

Question 12

A data analyst discovers an anomaly in a dataset. Which two actions should be taken before reporting? (Choose TWO.)

Accepted Answer

Verify the data source and extraction process. Option C is correct because verifying the data source and extraction process ensures the anomaly is not an artifact of how the data was pulled, transformed, or joined before it is escalated. Option D is correct because checking for data entry errors or technical glitches (e.g., sensor faults, ETL failures, or duplicate records) confirms whether the anomaly reflects a genuine signal or a data quality issue. Both steps establish validity and reproducibility of the finding, which is essential prior to reporting. Option A is wrong because assuming the anomaly is real without validation risks reporting a false positive. Option B is wrong because alerting all stakeholders immediately is premature before the anomaly is verified. Option E is wrong because removing the anomaly without documentation destroys evidence and violates data integrity and auditability practices.

Answer

Assume the anomaly is real and report it

Answer

Immediately alert all stakeholders

Answer

Remove the anomaly without documentation

Question 13

A data analyst is performing hypothesis testing to compare the mean sales of two store locations. Which TWO conditions must be satisfied to use a two‑sample t‑test? (Select TWO.)

Accepted Answer

The data is approximately normally distributed. The two-sample t-test assumes that the data in each group are approximately normally distributed. This is a key parametric assumption; if the sample sizes are large (typically n > 30), the Central Limit Theorem can relax this requirement, but for smaller samples, normality must hold to ensure valid test statistics and p-values.

Answer

The data is paired between the two locations

Answer

The sample sizes are equal

Answer

The variances of the two populations are equal

Question 14

A data analyst is building a narrative around a quarterly sales decline. The story should follow a narrative arc. Which THREE elements should be included in the story?

Accepted Answer

Resolution: By diversifying suppliers, sales recovered in November.. The question asks for the three elements of a narrative arc, which are Situation, Complication, and Resolution. Option C is correct because it establishes the Situation, the baseline context that Q3 sales were on track to meet targets, which sets the stage for the story. Option E is correct because it introduces the Complication, the supply chain disruption that caused the October drop, creating the conflict or turning point. Option B is correct because it provides the Resolution, explaining how diversifying suppliers led to recovery in November, which closes the narrative arc. Option A is not correct because a random color scheme for every chart harms visual consistency and does not contribute to a narrative arc. Option D is not correct because a detailed description of each product's sales breakdown is granular data detail, not a structural element of the narrative arc.

Answer

Random color scheme for every chart

Answer

Detailed description of each product's sales breakdown

Question 15

A data analyst frequently receives ad hoc requests for the same type of analysis. Which TWO approaches could reduce the number of ad hoc requests?

Accepted Answer

Create a scheduled report that covers the common analysis. Option B is correct because a scheduled report that covers the common analysis proactively delivers the recurring results the analyst keeps being asked for, eliminating the need for users to submit one-off requests each time. Option E is correct because a self-service BI tool lets business users build and run their own reports against governed data, shifting routine ad hoc demand away from the analyst. Option A is not correct because increasing data freshness to real-time addresses latency, not the volume of repetitive requests. Option C is not correct because adding security controls does not reduce how often users ask for the same analysis. Option D is not correct because ignoring requests is unprofessional and does nothing to eliminate the underlying recurring demand.

Answer

Increase data freshness to real-time

Answer

Add more security to the data

Answer

Ignore the requests until they become urgent

Question 16

Which TWO are examples of leading indicators in a business context? (Select two.)

Accepted Answer

Customer engagement score. Leading indicators are forward-looking metrics that predict future performance, and option C (customer engagement score) qualifies because engagement levels today tend to forecast future retention, loyalty, and purchasing behavior. Option D (number of qualified leads) is also a leading indicator since a healthy pipeline of qualified prospects predicts future sales and revenue before those deals close. By contrast, option A (employee turnover rate) is a lagging indicator because it measures past attrition that has already occurred, and options B (net profit margin) and E (monthly revenue) are lagging outcome metrics that report financial results after the fact rather than predicting them.

Answer

Employee turnover rate

Answer

Net profit margin

Answer

Monthly revenue

Question 17

An organization is implementing a data lake to store raw data from various sources. Which THREE characteristics are typically associated with a data lake compared to a data warehouse?

Accepted Answer

Supports batch and real-time processing. A is correct because a data lake is designed to handle both batch and real-time/streaming ingestion and processing, unlike a traditional data warehouse that is primarily optimized for batch ETL workloads. B is correct because a data lake stores data in its native/raw format (e.g., JSON, Parquet, CSV, images, logs) without forcing an upfront conversion. C is correct because a data lake applies schema-on-read, meaning the schema is defined when the data is queried rather than when it is written. D is incorrect because data lakes support structured, semi-structured, and unstructured data, not only structured data. E is incorrect because a data lake typically loads raw data as-is and defers transformation until read/query time, whereas a data warehouse usually requires transformation before loading.

Answer

Supports only structured data

Answer

Requires data transformation before loading

Question 18

A data analyst is performing EDA on a dataset with numerical features. Which methods are appropriate for identifying outliers? (Select TWO).

Accepted Answer

Z-score method. The Z-score method (C) is correct because it quantifies how many standard deviations a value lies from the mean, so observations with |z| above a threshold such as 3 (or 2.5) are flagged as outliers in numerical features. The Interquartile range (IQR) method (E) is also correct because it defines outliers as values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR, making it a robust, distribution-agnostic detection technique. Mean imputation (A) is not an outlier-detection method at all; it is a missing-value handling technique that replaces NaNs with the column mean. The Pearson correlation coefficient (B) measures the linear relationship between two variables, not the extremeness of individual values, so it cannot identify outliers. Standard deviation alone (D) only describes data spread and, without a rule like the Z-score, does not flag specific observations as outliers.

Answer

Mean imputation

Answer

Pearson correlation coefficient

Answer

Standard deviation alone

Question 19

Which TWO of the following are considered structured data?

Accepted Answer

A relational database table. A relational database table stores data in a predefined schema of rows and columns, where each column has a fixed data type. This rigid structure allows for efficient querying, indexing, and relational operations, making it a classic example of structured data.

Answer

A PDF report with free-form text

Answer

A JPEG image of a product

Answer

A JSON file with nested key-value pairs

Question 20

A data analyst is validating a dataset acquired from an external source. Which TWO actions are appropriate for data quality assessment?

Accepted Answer

Check for missing values in critical fields. Option A is correct because checking for missing values in critical fields is a core data quality assessment step that identifies nulls or gaps in essential attributes, which can skew analysis or break downstream processing. Option C is correct because validating data format against the expected schema confirms that each column's data type, structure, and constraints (e.g., date formats, numeric ranges, string lengths) match requirements, catching inconsistencies from the external source. Option B is not appropriate because deleting rows with null values without review can silently discard valid records and hide data quality issues rather than assessing them. Option D is wrong because loading unvalidated data directly into production risks propagating errors and corrupting downstream systems. Option E is wrong because transforming data to match the target system without verification skips the assessment step and can mask or introduce quality problems.

Answer

Delete any rows with null values without review

Answer

Immediately load all data into production

Answer

Transform data to match target system without verification