DA0-002 Practice Exam 64 — 20 Questions

Question 1

A data architect needs to store raw data from various sources, including social media feeds and log files, for future analysis. The data may be used for machine learning and ad-hoc queries. Which storage solution is most appropriate for storing raw data in its native format?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Data lake. A data lake is designed to store raw data in its native format, including unstructured and semi-structured data from sources like social media feeds and log files. It supports schema-on-read, making it ideal for future machine learning and ad-hoc queries without requiring upfront transformation. This aligns directly with the requirement to preserve raw data for flexible analysis.

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Data mart

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Relational database

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Data warehouse

Question 2

A data analyst is comparing the average test scores of students who attended a tutoring program versus those who did not. Which statistical test is most appropriate for determining if there is a significant difference between the means of these two independent groups?

Accepted Answer

Two-sample t-test. The independent samples t-test is used to compare the means of two independent groups.

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Paired t-test

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Chi-square test

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ANOVA

Question 3

A data analyst needs to determine whether the mean sales of two different regions are significantly different. The samples are independent and the data is normally distributed. Which statistical test should be used?

Accepted Answer

Independent samples t-test. The independent samples t-test is the correct choice because the scenario involves comparing the means of two independent groups (two different regions) with normally distributed data. This test specifically assesses whether the difference between the two sample means is statistically significant, assuming equal or unequal variances as determined by Levene's test.

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Chi-square test for independence

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ANOVA

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Paired t-test

Question 4

A data analyst is designing a data model for a sales data warehouse. The model should optimize query performance for aggregations by minimizing joins and duplicating data where necessary. Which schema design should the analyst use?

Accepted Answer

Star schema. Star schema denormalizes dimensions into a single table, reducing joins and improving query speed for aggregates. Snowflake schema normalizes dimensions increasing joins. Entity-relationship and 3NF are optimized for transactional systems, not analytical queries.

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Entity-relationship model

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Snowflake schema

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3NF normalized model

Question 5

A company has a dataset with 100 features. The data analyst wants to reduce dimensionality while preserving as much variance as possible. Which technique should be used?

Accepted Answer

PCA (Principal Component Analysis). PCA is the correct choice because it is an unsupervised linear dimensionality reduction technique that projects the data onto orthogonal components ordered by the variance they capture. By selecting the top principal components, the analyst can retain the maximum possible variance in the dataset while reducing the number of features from 100 to a smaller set, directly addressing the goal of preserving variance.

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LDA (Linear Discriminant Analysis)

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Autoencoders

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t-SNE

Question 6

A marketing team wants to segment customers into groups based on purchasing behavior without prior labels. Which algorithm should the data analyst use?

Accepted Answer

K-means clustering. K-means clustering is the correct choice because it is an unsupervised learning algorithm that groups unlabeled data into clusters based on feature similarity. Since the marketing team has no prior labels for customer segments, K-means can partition customers by purchasing behavior patterns, such as frequency and monetary value, without needing predefined categories.

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K-nearest neighbors

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Linear regression

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Decision tree

Question 7

A data analyst is preparing a data storytelling presentation for a non-technical audience. Which THREE techniques are most effective for communicating insights?

Accepted Answer

Using relevant visuals such as charts and graphs.. Data storytelling for non-technical audiences relies on visuals like charts and graphs to make complex data patterns immediately understandable, reducing cognitive load and enabling faster insight absorption. Effective visuals should be simple, clearly labeled, and directly tied to the narrative, avoiding clutter that could confuse the audience.

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Including raw data tables for reference.

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Adding complex statistical terms to demonstrate expertise.

Question 8

During data acquisition, an analyst notices that the data from an external vendor has inconsistent date formats. What is the first step the analyst should take?

Accepted Answer

Perform data profiling. Data profiling is the initial step to understand the structure, quality, and issues in the data. Rejecting or transforming without profiling may lead to errors, and contacting the vendor is premature without understanding the scope.

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Contact the vendor to request corrected data

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Immediately transform dates to a standard format

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Reject the entire dataset

Question 9

Refer to the exhibit. Based on the data profiling results, what is a likely data quality issue?

Accepted Answer

Validity. The min of 0 and max of 150 are biologically implausible for age. A 0-year-old or 150-year-old customer likely indicates invalid data, affecting validity.

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Completeness

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Accuracy

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Consistency

Question 10

A healthcare organization collects patient questionnaire data via paper forms at clinics. The forms are scanned and sent to a central office, where staff manually enter data into an electronic system. This process is slow and error-prone. The organization wants to reduce manual entry errors and speed up data availability. Which method should they adopt?

Accepted Answer

Use optical character recognition (OCR) to digitize the forms and automatically populate the database. Optical Character Recognition (OCR) can convert scanned images to text automatically, reducing manual entry errors and speeding up the process. Requiring patients to fill out online forms may not be feasible for all patients, especially those without internet access. Continuing manual entry with double-checking is still slow and labor-intensive. Sending to an external company introduces additional cost and potential privacy concerns.

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Continue manual entry but double-check all entries

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Send forms to an external data processing company

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Require patients to fill out forms online at home

Question 11

During data acquisition, a data engineer uses a tool to extract data from a source system incrementally based on a timestamp column. Which method is being used?

Accepted Answer

Change data capture (CDC). Change data capture (CDC) captures modifications since the last extraction. Full extraction retrieves all data each time, snapshot extracts a point-in-time copy, and manual is not automated.

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Snapshot extraction

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Full extraction

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Manual extraction

Question 12

An analyst creates a histogram of customer transaction amounts but observes that the distribution looks bimodal. Upon review, the analyst realizes that two different customer segments (retail and wholesale) were combined. Which action best addresses this?

Accepted Answer

Create two separate histograms, one for each segment. Splitting the data into two separate histograms by segment reveals the distinct distributions. Changing bin width may hide or exaggerate features. Combining into one chart with color is possible but may still obscure the bimodal nature if overlapping. A box plot shows medians but not shape.

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Use a single histogram with two colors for segments

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Use a box plot instead of a histogram

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Increase the number of bins to see more detail

Question 13

A data analyst is importing a CSV file that contains a mixture of numeric and text fields. What is the most common issue when importing?

Accepted Answer

Data types being incorrectly inferred. Data type inference often fails, causing numbers to be read as text or vice versa. File size limitations, missing headers, and duplicate rows are less common or not specific to mixed types.

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Duplicate rows

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Missing header row

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File size limitation

Question 14

An analyst is reviewing the above SQL query used to acquire data. What does this query retrieve?

Accepted Answer

Customers who placed more than 5 orders in 2023. The SQL query uses a HAVING clause with COUNT(*) > 5 to filter customers who placed more than 5 orders in 2023. The WHERE clause restricts records to the year 2023, and the GROUP BY customer_id aggregates orders per customer. The condition '> 5' explicitly excludes customers with exactly 5 or fewer orders, making option A correct.

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All customers who placed at least 5 orders in 2023

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The total number of orders per customer in 2023

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Customers who placed exactly 5 orders in 2023

Question 15

Refer to the exhibit. What is the most likely cause of the extraction failure?

Accepted Answer

The network firewall is blocking the port. Connection timeouts with consistent 30-second delays suggest the network firewall is blocking the port, causing the connection to hang until timeout. Option A is wrong because if the server were down, the error would be connection refused immediately. Option C is wrong because a complex query would cause a slow query, not a connection timeout. Option D is wrong because a locked table would cause a lock wait timeout, not a connection timeout.

Answer

The source table is locked

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The extraction query is too complex

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The database server is down

Question 16

A retail company wants to analyze customer purchase patterns to identify products frequently bought together. Which data mining technique is most appropriate?

Accepted Answer

Association rules. Association rules (market basket analysis) discover co-occurrence relationships. Regression predicts numeric values, clustering groups similar items, classification assigns categories.

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Classification

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Clustering

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Regression

Question 17

The exhibit shows a JSON configuration for a line chart that displays average response time over time. However, the chart shows jagged lines with many spikes. Which configuration change would smooth the visualization?

Accepted Answer

Change "interpolation" to "monotone". Setting interpolation to 'monotone' or 'smooth' can reduce jaggedness. The aggregation is already applied in the query, so changing it to 'average' in the widget would double-aggregate. Changing chart type or data source is not necessary.

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Change "aggregation" to "average"

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Change the query to include a moving average

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Change chartType to "bar"

Question 18

Refer to the exhibit. What is the best corrective action to resolve this error?

Accepted Answer

Convert the 'revenue' column to numeric data type during ETL. The error indicates a data type mismatch; converting the column to numeric in the ETL process ensures compatibility.

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Change the chart type to a bar chart

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Remove the 'revenue' column from the visualization

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Use a string-compatible chart type

Question 19

Which TWO are valid data acquisition methods? (Select two.)

Accepted Answer

Web scraping. Web scraping and API calls are direct methods to acquire data from external sources. Data profiling and data normalization are data preparation techniques, not acquisition. Data encryption is a security measure.

Answer

Data normalization

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Data encryption

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Data profiling

Question 20

A company is building a data pipeline to ingest sensor data from IoT devices. The data arrives continuously in small batches and must be processed in real-time for monitoring. Which type of data source best describes this scenario?

Accepted Answer

Streaming data. B is correct because the scenario describes data arriving continuously in small batches that must be processed in real-time for monitoring. This is the defining characteristic of streaming data, which is typically ingested via technologies like Apache Kafka, Amazon Kinesis, or MQTT brokers, enabling low-latency processing and immediate alerting.

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Transactional database

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Web scraping

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Flat file