AI0-001 AI Models and Data Engineering Practice Test 4 — 15 Questions

Question 1

A data engineer is designing a data pipeline for a real-time recommendation system. The pipeline must handle high velocity streams and ensure data quality. Which three components should be included in the pipeline? (Select THREE).

Accepted Answer

A stream processing engine like Apache Kafka Streams. Apache Kafka Streams is a correct choice because it is a stream processing library specifically designed for building real-time applications and microservices that process data in motion. For a high-velocity recommendation pipeline, it provides exactly-once semantics, stateful processing (e.g., windowed joins, aggregations), and seamless integration with Kafka topics, enabling low-latency transformations without requiring an external cluster.

Answer

A data warehouse for historical analysis

Answer

A batch processing framework like Apache Spark

Question 2

Refer to the exhibit. A data engineer is training a binary classification neural network. The loss fluctuates and does not converge. Which hyperparameter adjustment is most likely to stabilize training?

Accepted Answer

Decrease the learning rate. A high learning rate can cause the loss to oscillate and prevent convergence. Decreasing the learning rate typically stabilizes training. Increasing units, changing activation, or adding dropout may not address the root cause of fluctuation.

Answer

Change the activation to tanh

Answer

Add dropout after each layer

Answer

Increase the number of units in the first dense layer

Question 3

Refer to the exhibit. A data engineer runs a validation report on the customers table. The "income" column has 12 null values. Which imputation strategy is most appropriate for this column?

Accepted Answer

Replace nulls with the median income per region. Income varies by region, so imputing with the median per region accounts for regional differences. The mean of entire dataset may be skewed, 0 is inappropriate, and removing rows reduces sample size.

Answer

Remove rows with null income

Answer

Replace nulls with 0

Answer

Replace nulls with the mean income of the entire dataset

Question 4

Refer to the exhibit. A data engineer notices that the batch processing step is taking too long and causing delays. Which change would most likely reduce the latency?

Accepted Answer

Move feature engineering to the stream processing step in Flink. Moving feature engineering from the batch Spark job to the stream processing Flink job reduces the workload on the batch step, making it faster. Replacing Flink, increasing parallelism, or changing output format do not address the bottleneck as effectively.

Answer

Increase the parallelism of the Spark job

Answer

Replace Apache Flink with Apache Storm for stream processing

Answer

Change the output format from Parquet to CSV

Question 5

A data scientist is preparing a dataset for a supervised learning model. The dataset contains missing values in 15% of the rows for a numeric feature. Which preprocessing technique should be applied to minimize bias?

Accepted Answer

Impute missing values with the mean of the feature.. Imputing missing values with the mean preserves data size and is standard for numeric features when missingness is random. Removing rows can introduce bias if missingness is not random. Encoding as a separate category is for categorical features. Using a model that handles missing values natively is not always available and may still require imputation.

Answer

Remove all rows with missing values.

Answer

Encode the missing values as a separate category.

Answer

Use a model that handles missing values natively.

Question 6

A company is deploying an AI model to recommend products. The model's training data included historical purchases from the past two years, but the business environment has changed significantly due to a market shift. What is the most likely issue affecting model performance?

Accepted Answer

Concept drift. Concept drift occurs when the statistical properties of the target variable change over time, which is common in dynamic business environments. Overfitting and underfitting relate to training dataset characteristics. Data leakage involves using information not available at prediction time.

Answer

Overfitting

Answer

Underfitting

Answer

Data leakage

Question 7

An AI team notices that a model's F1 score on the validation set is 0.95, but on the test set it drops to 0.72. Which course of action is most appropriate?

Accepted Answer

Apply regularization.. The F1 score dropping from 0.95 on the validation set to 0.72 on the test set is a classic sign of overfitting, where the model has memorized the training/validation data but fails to generalize to unseen test data. Applying regularization (e.g., L1/L2 weight decay, dropout) is the most appropriate course of action because it penalizes overly complex models, reduces variance, and improves generalization without requiring more data or changing the model architecture.

Answer

Reduce the training dataset size.

Answer

Adjust the train/test split to be more representative.

Answer

Increase model complexity.

Question 8

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

Answer

Data warehouse

Answer

Relational database

Answer

Data lake

Question 9

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

Accepted Answer

Multicollinearity. Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.

Answer

Data leakage

Answer

Overfitting

Answer

Underfitting

Question 10

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Accepted Answer

Apply cost-sensitive learning. Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.

Answer

Reduce the majority class size

Answer

Use SMOTE to generate synthetic samples

Answer

Collect more data for the minority class

Question 11

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

Accepted Answer

Fine-tune the pre-trained model on domain data. Fine-tuning leverages the pre-trained model's knowledge and requires only minimal additional training on domain-specific data, making it efficient. Training from scratch is computationally expensive and requires large datasets. Using the model as-is may perform poorly on domain-specific language. Ensembling multiple models adds complexity without clear benefit.

Answer

Use the pre-trained model as is

Answer

Train a new model from scratch

Answer

Ensemble multiple pre-trained models

Question 12

A data pipeline processes customer data from multiple sources. The data quality check reveals duplicate records. Which step should the pipeline include to handle this?

Accepted Answer

Data deduplication. Deduplication is the process of identifying and removing duplicate records to ensure data quality. Data validation checks for schema or format errors. Data transformation changes data structure or values. Data encryption ensures security but does not address duplicates.

Answer

Data encryption

Answer

Data transformation

Answer

Data validation

Question 13

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

Accepted Answer

Quantization. Quantization reduces the precision of model weights (e.g., from float32 to int8), significantly speeding up inference and reducing memory footprint with minimal accuracy loss. Increasing batch size is for throughput, not single inference latency. Using a larger model or adding more layers would increase latency.

Answer

Increase batch size

Answer

Add more layers

Answer

Use a larger model

Question 14

A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)

Accepted Answer

Recall. Precision and Recall directly measure the model's ability to correctly identify positive (minority) instances and avoid false positives. Accuracy is misleading when classes are imbalanced. MSE is for regression. F1 score combines precision and recall, but the question asks for two metrics, and precision and recall are fundamental.

Answer

Accuracy

Answer

Mean squared error (MSE)

Answer

F1 score

Question 15

A data engineer is designing a feature store for machine learning. Which THREE components are essential for a feature store? (Choose THREE.)

Accepted Answer

Data ingestion pipeline. A feature store requires a feature repository to store feature definitions and values, an online serving layer to serve features at low latency for inference, and a data ingestion pipeline to continuously update features. Model registry and experiment tracking are important for MLOps but are not core components of a feature store.

Answer

Experiment tracking

Answer

Model registry