DA0-002 Practice Test 37 — 15 Questions

Question 1

A data team is integrating customer data from three sources. After joining, they find that the count of unique customers is lower than expected. What is the most likely cause?

Accepted Answer

Inconsistent key definitions.. When joining customer data from multiple sources, inconsistent key definitions (e.g., one source uses integer IDs while another uses string IDs, or different formats like 'CUST-001' vs '1001') cause the join to fail to match records that actually represent the same customer. This results in fewer unique customers than expected because the join treats mismatched keys as different entities, effectively dropping or misaligning records. The data team likely used an inner join or a left join that only retains matches based on exact key equality, so any key inconsistency reduces the count of matched unique customers.

Answer

Missing values in join keys.

Answer

Data truncation during transfer.

Answer

Duplicate entries across sources.

Question 2

A data analyst is performing a chi-square test of independence on a 2x2 contingency table. The p-value is 0.04. At α=0.05, which THREE of the following statements are correct?

Accepted Answer

There is a statistically significant association between the two variables.. Since p < α, reject the null hypothesis, meaning there is an association. The test does not measure strength (Cramer's V does) and does not identify specific categories.

Answer

The test indicates a strong association between variables.

Answer

The result is not statistically significant.

Question 3

A data analyst discovers a data discrepancy in a report that has already been distributed to executives. The discrepancy is due to a known bug that will be fixed next month. What is the most appropriate action?

Accepted Answer

Inform the executives of the error and its impact. Informing executives of the error and its impact maintains transparency and trust. Option A (send corrected report immediately) may cause confusion without proper context. Option B (wait for next scheduled update) delays communication and could lead to decisions based on incorrect data. Option C (ignore it) is unethical.

Answer

Send a corrected report immediately with an apology

Answer

Wait for the next scheduled update

Answer

Ignore it since it will be fixed soon

Question 4

A company receives daily sales data in CSV format. The data includes a 'Date' column in MM/DD/YYYY format. To load this into a database that expects YYYY-MM-DD, the analyst should:

Accepted Answer

Use a data transformation tool to convert the date format during ETL. (use a data transformation tool) is the standard practice during ETL to convert date formats programmatically. Option A (manually edit) is inefficient and error-prone. Option B (change schema) would require altering the database and may cause compatibility issues. Option C (ignore) loses data integrity.

Answer

Manually edit the CSV files before loading

Answer

Change the database schema to accept MM/DD/YYYY

Answer

Ignore the date column and use a default date

Question 5

A marketing team wants to analyze customer sentiment from social media posts. Which data acquisition method is most appropriate?

Accepted Answer

Web scraping from public social media APIs. Web scraping from public social media APIs allows direct access to public posts for sentiment analysis, which is exactly what the marketing team needs. Option A is wrong because internal databases typically do not contain social media data. Option B is wrong because physical sensors are unrelated to social media sentiment. Option D is wrong because survey questionnaires are not real-time and do not capture existing social media posts.

Answer

Internal database query

Answer

Physical sensor data

Answer

Survey questionnaire

Question 6

A data analyst is evaluating data quality issues during acquisition. Which TWO issues are most likely to arise from merging data from different sources? (Select exactly 2)

Accepted Answer

Duplicate records. Options B and E are correct because merging data from different sources often leads to duplicate records (the same entity represented differently across sources) and formatting inconsistencies (such as different date formats, units of measurement, or naming conventions). Option A is wrong: user access permissions are a security concern, not a direct data quality issue arising from merging. Option C is wrong: slow network speed is a performance or infrastructure issue, not a data quality issue. Option D is wrong: high storage cost is a cost consideration, not a data quality issue.

Answer

User access permissions

Answer

Slow network speed

Answer

High storage cost

Question 7

A data team is using web scraping to collect competitor pricing data. The target website has anti-scraping measures like CAPTCHAs and rate limiting. Which approach is most effective?

Accepted Answer

Use rotating proxies and respectful delays. Using rotating proxies and respectful delays helps evade anti-scraping mechanisms like CAPTCHAs and rate limiting by distributing requests across multiple IPs and mimicking human browsing behavior. Option A is incorrect because using a single IP address makes it easy for the website to block all requests from that IP. Option B is incorrect because disregarding robots.txt may violate the website's terms of service and could lead to legal action or IP bans. Option D is incorrect because increasing request frequency would trigger rate limiting and increase the likelihood of being blocked.

Answer

Use a single IP address

Answer

Disregard robots.txt

Answer

Increase request frequency

Question 8

A data analyst notices that a column labeled 'Income' contains values like '$50,000' and '$75,000', but also 'High' and 'Low'. What data concept issue is occurring?

Accepted Answer

Mixing quantitative and qualitative data. The 'Income' column contains both numeric values (e.g., '$50,000', '$75,000') which are quantitative data, and categorical labels ('High', 'Low') which are qualitative data. Mixing these two distinct data types in a single column violates data consistency principles and prevents proper statistical analysis or machine learning processing. This is a classic example of mixing quantitative and qualitative data.

Answer

Mixing discrete and continuous data

Answer

Mixing nominal and ordinal data

Answer

Mixing structured and unstructured data

Question 9

Refer to the exhibit. What is the most likely issue causing the unexpectedly low count?

Accepted Answer

The signup_date column is in a different date format. If the signup_date column is stored in a different date format (e.g., MM/DD/YYYY), the comparison with '2023-01-01' (YYYY-MM-DD) may not match many records, leading to an unexpectedly low count. Option A is incorrect because indexing affects performance, not the correctness of the count. Option B is incorrect because COUNT(*) aggregates all rows and does not require a GROUP BY clause. Option C is incorrect because a database refresh typically updates data but does not directly cause a mismatch in date comparisons.

Answer

The customers table is indexed incorrectly

Answer

The query is missing a GROUP BY clause

Answer

The database was not refreshed

Question 10

A company is designing a dashboard for real-time monitoring. Which THREE considerations are most critical?

Accepted Answer

Alert thresholds. Alert thresholds (B) are critical for real-time monitoring because they define the conditions that trigger notifications when metrics exceed or fall below acceptable ranges. Without thresholds, the dashboard cannot proactively alert operators to anomalies, defeating the purpose of real-time oversight. This directly supports the domain of communicating data insights by ensuring actionable alerts are delivered promptly.

Answer

Color palette aesthetics

Answer

Drill-down capability

Question 11

A company needs to visualize the trend of monthly sales revenue over the past two years. Which chart type is most appropriate?

Accepted Answer

Line chart. (line chart) is correct because line charts are best for showing trends over time. Option A (bar chart) is more for comparing categories, Option B (scatter plot) shows relationships between variables, and Option D (pie chart) shows parts of a whole.

Answer

Bar chart

Answer

Scatter plot

Answer

Pie chart

Question 12

A dataset contains salaries ranging from $25,000 to $2,500,000, with most salaries under $100,000. Which chart type best shows the distribution without distortion from extreme values?

Accepted Answer

Box plot. Box plot. Box plots display the distribution through quartiles and explicitly show outliers as individual points, making them ideal for skewed data with extreme values. In contrast, a scatter plot (B) is for relationships between variables, not distribution; a histogram (C) with equal bin widths would compress the majority of the data into a single bin if bins are sized to include the extreme values; and a line chart (D) is for trends over time. Therefore, the box plot best shows the distribution without distortion from extreme values.

Answer

Scatter plot

Answer

Histogram with equal bin widths

Answer

Line chart

Question 13

A dashboard designer wants to highlight the sales performance of individual sales representatives compared to team averages. Which chart type is most suitable for this comparison?

Accepted Answer

Bar chart with average line. A bar chart with an average line is ideal for comparing individual sales representatives' performance to the team average because each bar clearly shows the magnitude of each value, and the average line provides a reference for comparison. This makes it easy to see who is above or below average. In contrast, a scatter plot (A) is used for correlation between two variables, a pie chart (C) shows proportions of a whole, and a line chart (D) shows trends over time, none of which directly compare individual values to an average.

Answer

Scatter plot

Answer

Pie chart

Answer

Line chart

Question 14

A financial services company is migrating its customer data from a legacy on-premises relational database to a cloud-based data warehouse. The legacy database uses a denormalized schema with a single table 'customer_master' that contains all customer attributes, including repeated groups for multiple accounts per customer (account1_type, account1_balance, account2_type, account2_balance, etc.). The data warehouse team wants to implement a normalized star schema with separate dimension and fact tables. During the ETL process, the team encounters an error: 'Data truncation: string data right truncation' when loading account_type values into the dim_account table. The account_type column in dim_account is defined as VARCHAR(10), but the source data contains account types like 'SavingsPlus' (11 characters) and 'CheckingPremium' (15 characters). The team must resolve this issue without losing data. Which course of action should the team take?

Accepted Answer

Increase the VARCHAR length of dim_account.account_type to accommodate the longest account type.. Increasing the VARCHAR length of dim_account.account_type to accommodate the longest account type (e.g., VARCHAR(15) for 'CheckingPremium') resolves the data truncation error without data loss. This aligns with the star schema design principle of preserving source data integrity while ensuring the column definition matches the actual data length. The team must avoid truncation or NULL insertion to maintain accurate dimensional attributes for analytics.

Answer

Truncate the account_type values to 10 characters during ETL.

Answer

Change the data type of dim_account.account_type to TEXT.

Answer

Ignore the error and continue loading with NULL values for truncated rows.

Question 15

A company needs to store user session data for a web application. Each session has a unique session ID, and the data must be retrieved very quickly by session ID. The data does not require complex relationships or transactions. Which type of NoSQL database is most appropriate?

Accepted Answer

Key-value store (e.g., Redis). A key-value store like Redis is the most appropriate choice because it is optimized for extremely fast lookups by a unique key (session ID) and does not require complex relationships or transactions. Redis stores data in memory, providing sub-millisecond retrieval times ideal for session management, and supports built-in expiration (TTL) to automatically clean up stale sessions.

Answer

Wide-column store (e.g., Cassandra)

Answer

Document store (e.g., MongoDB)

Answer

Graph database (e.g., Neo4j)