DA0-002 Practice Test 2 — 10 Questions

Question 1

A data analyst is tasked with collecting data from a web API that returns JSON. The API requires an API key in the header. Which method should be used to authenticate?

Accepted Answer

Add the API key in the HTTP header as 'Authorization: Bearer '. The correct method is to include the API key in the HTTP header using the 'Authorization: Bearer ' format. This is the standard approach for token-based authentication in REST APIs, as defined by RFC 6750. It keeps the credential out of URLs and logs, and is the expected mechanism for API key authentication in modern web APIs.

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Use a session cookie

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Store the API key in the database and reference it

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Include the API key in the URL query string

Question 2

A data scientist is preparing data for a K-means clustering algorithm. The dataset contains features measured in different units (e.g., income in dollars and age in years). Which preprocessing step is most critical before running K-means?

Accepted Answer

Standardize or normalize the features. K-means is sensitive to the scale of features because it uses Euclidean distance. Min-max normalization or standardization ensures all features contribute equally.

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Remove outliers

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Encode categorical variables

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Perform feature selection

Question 3

A data analyst is performing data profiling on a customer table. Which TWO of the following are key metrics to assess data quality? (Select TWO.)

Accepted Answer

Minimum and maximum values. Null counts indicate missing values, and min/max values can reveal outliers or unexpected ranges. Row count alone doesn't assess quality; cardinality and data type verification are also important but the question asks for key metrics among the options.

Answer

Row count

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Cardinality

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Data type verification

Question 4

A retail company operates 50 stores across the country. The data analyst has been asked to create a dashboard to visualize monthly sales trends over the past two years and compare the performance of the top 5 stores. The dataset includes store name, date, and daily sales amount. Initial exploration reveals that some stores have missing sales data for certain months due to system outages, and there are occasional extreme values caused by promotional events (e.g., Black Friday sales are 10x normal). The analyst needs to choose an appropriate visualization approach that accurately represents the trends and comparisons while handling these data quality issues. What should the analyst do to best meet the requirements?

Accepted Answer

Use a line chart for the overall monthly sales trend (aggregated across all stores) and a separate bar chart for the top 5 stores by total sales, with tooltips explaining outlier values.. It combines a line chart for overall trend (aggregating all stores) and bar charts for top 5 stores, which allows comparison while handling missing data through aggregation. Tooltips can provide context for outliers. Option A is wrong because using separate line charts for each store becomes cluttered with 50 stores, and excluding outliers may omit valuable information about promotions. Option B is wrong because filtering out stores with missing data could remove important stores and distort the trend. Option D is wrong because a stacked bar chart with all 50 stores would be difficult to read and doesn't show trends over time effectively.

Answer

Use a line chart for each store's monthly sales and exclude outliers from the dataset.

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Use a bar chart showing monthly sales for each month and filter out stores with missing data.

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Use a stacked bar chart with all 50 stores, each bar representing a month.

Question 5

A large online retailer stores customer orders in a PostgreSQL database. Each order has a unique order ID, and the database is normalized to 3NF. Which type of data is this?

Accepted Answer

Structured data. The data is structured because it resides in a normalized PostgreSQL database with a unique order ID and conforms to a fixed schema (3NF). Structured data is organized into rows and columns with defined data types, enabling efficient SQL querying and ACID compliance. PostgreSQL's relational model enforces this structure through tables, constraints, and indexes.

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Semi-structured data

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Unstructured data

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Metadata

Question 6

A business analyst wants to show the relationship between advertising spend (independent variable) and sales revenue (dependent variable) for 12 different regions. Each region will be represented by a point. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart type because it directly visualizes the relationship between two continuous variables—advertising spend (independent) and sales revenue (dependent)—by plotting each of the 12 regions as a point. This allows the analyst to assess correlation, trends, and outliers, which is the core purpose of scatter plots in data visualization.

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Line chart

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Bar chart

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Histogram

Question 7

An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?

Accepted Answer

Ridge regression (L2). Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.

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Lasso regression (L1)

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Principal component analysis (PCA)

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Stepwise selection

Question 8

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. Data lakes are designed to ingest and process data in both batch and real-time streaming modes, unlike data warehouses which primarily handle batch ETL workloads. This flexibility is enabled by technologies like Apache Kafka for streaming and Apache Spark for batch processing, allowing the data lake to support diverse ingestion patterns without pre-schematization.

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Supports only structured data

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Requires data transformation before loading

Question 9

A company is merging two customer databases from different acquisitions. They need to identify duplicate records. Which data profiling technique is most effective?

Accepted Answer

Fuzzy matching on name and address. Fuzzy matching on name and address is the most effective technique because customer databases from different acquisitions often contain variations in spelling, formatting, and abbreviations (e.g., 'Bob' vs. 'Robert', 'St.' vs. 'Street'). Exact matching would miss these duplicates, while fuzzy matching uses algorithms like Levenshtein distance or Jaro-Winkler to quantify similarity and identify near-matches, ensuring comprehensive deduplication.

Answer

Manually compare all records

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Exact match on customer names

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Use primary keys from each database

Question 10

A company needs to store data that is highly interconnected, such as social network relationships. Which two database types are best suited for this? (Select TWO.)

Accepted Answer

Document store (e.g., MongoDB). Graph databases excel at storing relationships, and NoSQL databases support flexible schemas for interconnected data.

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Relational database

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

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Key-value store (e.g., Redis)