AI0-001 Practice Test 3 — 10 Questions

Question 1

A developer is integrating an AI microservice that accepts image uploads and returns classification labels. The service must handle spikes of up to 1,000 requests per minute but average 100 requests per minute. Which deployment architecture BEST meets these requirements with cost efficiency?

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

Accepted Answer

Use an async processing queue (e.g., RabbitMQ) with a pool of worker instances that auto-scale based on queue depth. An async queue with auto-scaling workers decouples ingestion from processing, absorbs bursts by buffering requests, and scales worker count based on queue depth — so you only pay for capacity during actual load. This matches the 10x peak-to-average ratio cost-effectively. Synchronous designs either over-provision for peak or drop requests during spikes.

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Expose the model via a serverless function (e.g., AWS Lambda) with synchronous invocation

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Deploy the service as a synchronous REST API on a single always-on VM sized for peak load

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Stream results directly from the model to the client using WebSockets

Question 2

An organization is deploying a large language model on-premises for compliance reasons. They need to serve inference requests with low latency. Which architecture should they use?

Accepted Answer

Containerize the model and deploy it on a Kubernetes cluster with autoscaling. Containerizing the model and deploying it on a Kubernetes cluster with autoscaling is the correct architecture because it provides horizontal scaling, low-latency inference through load-balanced pods, and supports on-premises deployment for compliance. Kubernetes can automatically scale replicas based on CPU/memory utilization or custom metrics (e.g., request queue depth), ensuring consistent response times under varying load.

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Use a batch processing system like Apache Spark

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Use a serverless function like AWS Lambda

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Deploy the model as a REST API on a single powerful server

Question 3

A company uses a pre-trained language model for a legal document classification task. They have limited labeled data (500 documents). Which strategy is MOST effective for adapting the model to this domain?

Accepted Answer

Fine-tune the pre-trained model on the 500 labeled documents.. Fine-tuning a pre-trained language model on 500 labeled legal documents is the most effective strategy because it leverages the model's existing knowledge of language structure and general semantics, requiring only a small amount of domain-specific data to adapt to the legal classification task. This approach avoids the high data requirements of training from scratch and outperforms rule-based or augmentation-only methods by directly optimizing the model's weights for the target domain.

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Use a rule-based keyword matching system instead.

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Train a new model from scratch on the 500 documents.

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Apply extensive data augmentation to increase dataset size.

Question 4

A startup is building a chatbot for customer service. They have 500 recorded conversations and want to use a pre-trained language model to generate responses. However, they have limited computational resources and need the chatbot to respond in real-time. They are considering fine-tuning a large model like GPT-3 or using a smaller model like DistilBERT. The conversation data contains industry-specific jargon. Which approach should they take?

Accepted Answer

Fine-tune DistilBERT on the conversation data. Fine-tuning DistilBERT on the 500 recorded conversations allows the model to adapt to industry-specific jargon while maintaining real-time responsiveness due to its smaller size. DistilBERT is a distilled version of BERT that retains 97% of BERT’s language understanding with 40% fewer parameters, making it suitable for limited computational resources. Fine-tuning on domain-specific data is essential here, as pre-trained models like GPT-3 lack exposure to the startup’s specialized terminology, and using a smaller model ensures low-latency inference for real-time chatbot responses.

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Use GPT-3 via API without fine-tuning

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Train a custom RNN from scratch on the conversations

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Implement a rule-based system with keywords

Question 5

A team uses Apache Kafka to stream real-time sensor data for ML inference. They need to process the stream, perform feature engineering, and store results in a data lake. Which tool is best suited for this streaming ML pipeline?

Accepted Answer

Apache Spark with Structured Streaming. Apache Spark with Structured Streaming is best suited because it provides a unified, scalable engine for both stream processing and batch processing, enabling real-time feature engineering on Kafka streams and direct writing to a data lake (e.g., Parquet format in Amazon S3). Its micro-batch or continuous processing model integrates natively with Kafka, allowing exactly-once semantics and low-latency transformations for ML inference pipelines.

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Apache Airflow

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TensorFlow Data Validation

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SageMaker Processing jobs

Question 6

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. Imputing missing income values with the median per region preserves the central tendency of each regional subgroup, which is robust to outliers and maintains the distributional characteristics of the data. This strategy is particularly appropriate for income data, which often exhibits skewness and regional variation, ensuring that the imputed values are contextually relevant and do not distort downstream analytics or machine learning models.

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Remove rows with null income

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Replace nulls with 0

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Replace nulls with the mean income of the entire dataset

Question 7

A machine learning engineer has a dataset of 100,000 records. She splits it into 70% training, 15% validation, and 15% test sets. After training, the model achieves 95% accuracy on training and 85% on validation. What does the accuracy difference most likely indicate?

Accepted Answer

The model is overfitting. The 10% gap between training accuracy (95%) and validation accuracy (85%) is a classic sign of overfitting. The model has memorized patterns specific to the training set rather than learning generalizable features, causing it to perform worse on unseen validation data. In machine learning, a significant drop in performance from training to validation indicates poor generalization, which is the hallmark of overfitting.

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The validation set is too small

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The model generalizes well

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The test set should be larger

Question 8

A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?

Accepted Answer

Median imputation. Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.

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Mean imputation

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Mode imputation

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Remove rows with missing values

Question 9

A company has a TensorFlow model trained on-premises and wants to deploy it on AWS SageMaker for scalable inference. What is the BEST way to package the model for deployment?

Accepted Answer

Package the model in a Docker container with a TensorFlow serving script and push to Amazon ECR. SageMaker expects models in a container format; the inference container should include the model artifacts and the serving code, allowing SageMaker to host it on scalable endpoints.

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Convert the model to ONNX and upload to SageMaker

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Upload the .h5 file to S3 and create a SageMaker endpoint directly

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Use SageMaker Studio to train the model again from scratch

Question 10

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

Accepted Answer

Right to explanation of decisions. Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For healthcare AI systems, this means patients have the right to obtain an explanation of the decision reached by the algorithm, such as how a diagnosis or treatment recommendation was derived. This requirement ensures transparency and accountability in high-stakes automated decisions.

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Explicit consent from all data subjects

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Data retention period of at least 10 years