AI Associate Data for AI Practice Test 3 — 15 Questions

Question 1

A company is preparing customer data for a predictive model. They notice that many records have missing values for the 'annual income' field. Which approach is best to handle this issue while minimizing bias?

Accepted Answer

Use model-based imputation considering other features.. Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.

Answer

Remove all records with missing values.

Answer

Replace missing values with the mean.

Answer

Set missing values to zero.

Question 2

A team is labeling text data for a sentiment analysis model. To ensure consistency and quality, which practice should they prioritize?

Accepted Answer

Use majority voting among multiple labelers.. Majority voting among multiple labelers reduces individual bias and errors, improving label consistency and quality for training data. This approach is standard in supervised learning for sentiment analysis because it aggregates diverse judgments, leading to more reliable ground truth labels.

Answer

Use a single expert labeler for all data.

Answer

Label all data by a single expert labeler.

Answer

Allow each labeler to interpret guidelines freely.

Question 3

For a real-time AI application that requires low-latency access to customer interaction data, which storage solution is most appropriate?

Accepted Answer

In-memory data store.. In-memory data stores (e.g., Redis, Memcached) store data in RAM rather than on disk, providing sub-millisecond read/write latencies essential for real-time AI applications that need immediate access to customer interaction data. This eliminates disk I/O bottlenecks and enables high-throughput, low-latency data retrieval for time-sensitive inference or decision-making.

Answer

Flat files on a network drive.

Answer

Relational database with complex joins.

Answer

Data lake with batch processing.

Question 4

A company wants to use customer purchase history to train a recommendation model. Which action is essential to comply with data privacy regulations?

Accepted Answer

Obtain explicit consent from customers.. Option C is correct because data privacy regulations such as GDPR and CCPA require a lawful basis for processing personal data, and explicit consent is a primary lawful basis when using customer purchase history for training a recommendation model. Without obtaining explicit consent, the company would be processing personal data without a valid legal ground, violating regulations that mandate transparency and user control over their data.

Answer

Use only publicly available data.

Answer

Ignore regulations because data is internal.

Answer

Anonymize the data after training.

Question 5

A data pipeline fails intermittently when processing large CSV files. The error log shows 'OutOfMemoryError'. Which configuration change is most likely to resolve this?

Accepted Answer

Increase the heap memory for the processing application.. The OutOfMemoryError indicates that the Java Virtual Machine (JVM) heap space is exhausted while processing large CSV files. Increasing the heap memory (e.g., using -Xmx flag) allocates more memory to the application, allowing it to handle larger datasets without crashing. This directly addresses the root cause of insufficient memory for the data pipeline's processing workload.

Answer

Use a smaller file size limit.

Answer

Increase the number of worker threads.

Answer

Switch to XML format.

Question 6

Which data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?

Accepted Answer

One-hot encoding.. One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.

Answer

Normalization.

Answer

Principal component analysis.

Answer

Standardization.

Question 7

A dataset contains a 'date' column. Which feature engineering technique would best capture both long-term trends and seasonal patterns?

Accepted Answer

Create cyclic features (sin/cos of month, day).. Option C is correct because cyclic features using sine and cosine transformations preserve the circular nature of temporal data (e.g., month 12 and month 1 are adjacent, not far apart). This allows a model to learn both long-term trends (via the year component) and seasonal patterns (via the cyclic encoding of month and day) without imposing a false linear ordering. In contrast, simple numeric extraction treats time as linear, which can misrepresent seasonal cycles.

Answer

Extract year, month, day as separate features.

Answer

Use only the day of week.

Answer

Drop the date column.

Question 8

Which method is most suitable for ingesting streaming data from IoT sensors into a data lake?

Accepted Answer

Real-time streaming with Apache Kafka.. Apache Kafka is the most suitable option because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. IoT sensors generate continuous, high-velocity data streams, and Kafka's publish-subscribe model allows data to be ingested into a data lake with low latency, ensuring near-real-time availability for analytics.

Answer

Copying data via FTP.

Answer

Batch ingestion every 24 hours.

Answer

Manual upload via web interface.

Question 9

A global company needs to ensure that customer data used for AI models complies with multiple regional regulations (GDPR, CCPA, LGPD). Which data governance practice is most effective?

Accepted Answer

Use a unified data catalog with tagging and classification.. Option D is correct because a unified data catalog with tagging and classification allows the organization to manage data governance and compliance across regions consistently.

Answer

Apply the strictest regulation globally.

Answer

Store all data in a single data warehouse.

Answer

Allow each region to manage its own data separately.

Question 10

Which TWO data preparation steps are critical for ensuring high-quality training data?

Accepted Answer

Removing duplicate records.. Option B is correct because duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.

Answer

Increasing dataset size by adding noise.

Answer

Normalizing all features.

Answer

Using only labeled data.

Question 11

Which THREE are key dimensions of data quality that directly impact AI model performance?

Accepted Answer

Consistency.. Consistency is a key dimension of data quality because AI models rely on stable patterns in the data. If the same entity is represented differently across records (e.g., 'NY' vs 'New York'), the model may learn incorrect correlations, leading to degraded prediction accuracy and unreliable outputs.

Answer

Timeliness.

Answer

Data volume.

Question 12

Which TWO considerations are important when labeling data for a supervised learning model?

Accepted Answer

Maintaining consistent guidelines.. Maintaining consistent guidelines (A) is critical because supervised learning models learn patterns from labeled data; inconsistent labels introduce noise and confuse the model, degrading its accuracy. Labeler expertise (B) ensures that domain-specific nuances are correctly captured, which is especially important for tasks like medical imaging or legal document classification where errors have high cost.

Answer

Using automated labeling for all tasks.

Answer

Ignoring inter-labeler agreement.

Answer

Labeling only a small sample.

Question 13

Refer to the exhibit. A data access policy is defined for a customer data set. Which statement best describes this policy?

Accepted Answer

Only users in the EU region can read customer data, with emails and phones partially masked.. Option B is correct because the policy shown in the exhibit applies a row-level security filter that restricts access to customer data based on the user's region, and a column-level masking rule that partially masks email and phone fields. Only users whose region attribute is set to 'EU' can view the rows, and even then, the email and phone columns are obfuscated (e.g., j***@example.com, ***-***-1234). This matches the behavior of a data masking policy combined with a regional access filter, which is a common pattern in data platforms like Snowflake or AWS Lake Formation.

Answer

All users can read customer data.

Answer

All users can read customer data but only those in the EU can see unmasked emails.

Answer

Only users in the EU region can read customer data, with emails and phones fully hidden.

Question 14

Refer to the exhibit. The data pipeline is failing. What is the most likely cause?

Accepted Answer

Schema mismatch between source and target.. Option D is correct because a schema mismatch between source and target is the most common cause of pipeline failures in data integration workflows. When the source data structure (e.g., column names, data types, or nested fields) does not match the target schema, the pipeline cannot map or transform the data correctly, leading to errors during ingestion or transformation stages. This is especially relevant in tools like Apache NiFi, AWS Glue, or Azure Data Factory, where schema validation is enforced at runtime.

Answer

Network timeout.

Answer

Missing required field in source data.

Answer

Insufficient memory.

Question 15

Refer to the exhibit. A developer runs a SOQL query. What does the output indicate?

Accepted Answer

The query returned 10 records in total.. The SOQL query output shows '10 records returned' with no error or partial result indicator, confirming that the query completed successfully and returned exactly 10 records. In Salesforce SOQL, the query result includes a 'totalSize' field that reflects the total number of records matching the query criteria, and here it matches the number of records displayed, indicating a complete and successful retrieval.

Answer

The query is still processing.

Answer

The output is incomplete.

Answer

The query failed.