AI0-001 Implementing AI Solutions Practice Test 6 — 15 Questions

Question 1

A company is deploying an LLM-based chatbot that must output responses in a structured JSON format for downstream processing. Which THREE prompt engineering techniques should the team use to ensure the output is valid and correctly structured? (Select three.)

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

Accepted Answer

Include few-shot examples of correct JSON outputs. A system prompt defining JSON structure, few-shot examples of valid JSON, and JSON mode in the model API all help produce valid structured output. Chain-of-thought and temperature adjustment do not directly enforce JSON format.

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Set temperature to 0 to increase determinism

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Use chain-of-thought prompting to reason before output

Question 2

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

Accepted Answer

The data contains complex, non-linear patterns that are not easily captured by rules. AI techniques like neural networks or gradient-boosted trees excel at capturing complex, non-linear interactions in high-dimensional data (e.g., purchase sequences combined with demographics) that rule-based systems cannot express without an explosion of brittle, hand-crafted conditions. This makes AI the better choice when the underlying patterns are not linearly separable or easily codified as if-then logic.

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The segmentation criteria are well-understood and can be expressed in simple if-then rules

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The segmentation must be fully explainable to regulators

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The team has no access to labeled data

Question 3

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Fine-tune a base LLM on the policy documents monthly

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Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 4

During data preparation for a classification model, the data scientist notices that one class has 95% of the samples and the other has only 5%. Which technique is MOST appropriate to address this imbalance?

Accepted Answer

Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class. Resampling techniques like SMOTE generate synthetic samples for the minority class or undersample the majority class, directly addressing class imbalance.

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Shuffle the data randomly before each training epoch

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Remove the minority class samples entirely

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Use a larger learning rate to force the model to pay attention to the minority class

Question 5

A team is deploying a fine-tuned LLM for code generation. They need to ensure the model output is always valid JSON. Which prompt engineering technique should they use?

Accepted Answer

Few-shot examples of valid JSON outputs. Few-shot prompting provides the model with examples of valid JSON outputs, conditioning it to mimic the format in its response. This is an effective technique for constraining output structure without requiring model retraining or special modes.

Answer

Chain-of-thought prompting

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Temperature setting to 0

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Using a larger model variant

Question 6

A data science team is building a model to detect fraudulent transactions. They have a dataset of 1 million normal transactions and 1,000 fraudulent ones. What is the MOST effective data preparation step to handle this imbalance?

Accepted Answer

Apply SMOTE to generate synthetic fraudulent transactions and randomly undersample normal transactions. Combining oversampling of the minority class (e.g., SMOTE) with undersampling of the majority class is a common and effective approach to balance the dataset.

Answer

Delete all normal transactions until the dataset is balanced

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Duplicate the fraudulent transactions 1,000 times

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Train the model on the original dataset; class imbalance does not affect model performance

Question 7

Which stage of the AI project lifecycle involves splitting data into training, validation, and test sets?

Accepted Answer

Data preparation. Data preparation includes splitting data, cleaning, normalization, and preventing leakage.

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Model evaluation

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Data acquisition

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Problem definition

Question 8

A developer is implementing a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model produces normalized vectors. Which metric is computationally efficient and equivalent to cosine similarity for normalized vectors?

Accepted Answer

Dot product. For normalized vectors, dot product is equivalent to cosine similarity and is faster to compute.

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Euclidean distance

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Hamming distance

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Manhattan distance

Question 9

A team is fine-tuning a large language model using LoRA. They have limited GPU memory. Which technique can further reduce memory consumption while maintaining similar fine-tuning quality?

Accepted Answer

Use QLoRA with 4-bit quantization of the base model. QLoRA combines 4-bit quantization of the base model with LoRA adapters, drastically reducing memory usage while preserving fine-tuning performance.

Answer

Fine-tune all layers instead of using LoRA

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Increase the rank of LoRA adapters

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Use a larger batch size

Question 10

An AI agent is designed to book flights by calling an external API. The agent must decide which tool to call based on user input, then generate the correct API parameters. Which pattern is MOST appropriate for this workflow?

Accepted Answer

ReAct pattern with tool descriptions and function calling. The ReAct (Reasoning + Acting) pattern interleaves reasoning steps with tool calls, allowing the agent to decide when to call a function and what arguments to use.

Answer

Chain-of-thought prompting only

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Zero-shot prompting with JSON mode

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Simple prompt with no tool descriptions

Question 11

Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?

Accepted Answer

Dense embeddings from a pre-trained transformer model. Dense embeddings represent semantic meaning in a continuous vector space, ideal for similarity search in RAG.

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Bag-of-words vectors

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TF-IDF vectors

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One-hot encoding

Question 12

A company is building a recommendation system for an e-commerce site. They have historical user-item interaction data. Which approach is most appropriate?

Accepted Answer

Train a collaborative filtering model on user-item interactions. Collaborative filtering uses user-item interactions to recommend items based on patterns from similar users or items, without requiring content features.

Answer

Use a large language model to generate random product suggestions

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Use a pre-trained image classification model to recommend visually similar products

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Deploy a rule-based system that always recommends best-selling items

Question 13

A data scientist is training a binary classifier and observes that the training accuracy is 99% but the test accuracy is only 70%. Which of the following is the MOST likely cause?

Accepted Answer

The model is overfitting the training data. A large gap between training and test accuracy indicates overfitting: the model memorized training data but fails to generalize.

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The model is underfitting the training data

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The learning rate is too high

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The test set contains data leakage from the training set

Question 14

Which chunking strategy for RAG is MOST appropriate when documents have a natural hierarchical structure (e.g., sections, subsections)?

Accepted Answer

Hierarchical chunking that preserves document structure. Hierarchical chunking respects the document structure, preserving context and enabling retrieval at different levels (e.g., section or paragraph).

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Fixed-size chunking with no overlap

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Semantic chunking based on sentence boundaries

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Random chunking with varying sizes

Question 15

A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)

Accepted Answer

Streaming responses for real-time inference. AI microservices are deployed as independent services, and streaming responses are needed for real-time video processing.

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Batch processing with nightly jobs

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Synchronous request-response with long timeouts

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Monolithic application deployment