Generative AI Leader Fundamentals of Generative AI Practice Test 3 — 15 Questions

Question 1

During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

Accepted Answer

Add a re-ranking step using a cross-encoder model to refine the top results.. Re-ranking with a cross-encoder model evaluates query-document pairs more deeply, improving precision at the cost of latency. Increasing the number of documents may introduce more noise. Changing embedding dimension or chunk size may help but are less targeted.

Answer

Increase the number of documents retrieved from the vector store.

Answer

Use a different embedding model with higher vector dimension.

Answer

Decrease the chunk size of documents to reduce noise.

Question 2

A team is training a custom foundation model using JAX on TPUs on Google Cloud. They encounter frequent Out of Memory (OOM) errors. Which action is most effective in resolving the OOM error?

Accepted Answer

Enable model parallelism using GSPMD to distribute the model across TPU cores.. Option D is correct because OOM errors when training large foundation models on TPUs often stem from the model exceeding the memory of a single TPU core. GSPMD (Generalized SPMD) enables automatic model parallelism, sharding the model's parameters, gradients, and optimizer states across multiple TPU cores, thereby reducing per-core memory pressure without altering the model architecture or precision.

Answer

Reduce the model size by decreasing the number of layers.

Answer

Increase the batch size to maximize TPU utilization.

Answer

Use mixed precision training (bfloat16) to reduce memory footprint.

Question 3

A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?

Accepted Answer

Model's license (proprietary vs open-source).. Option A is correct because the model's license determines whether the generated code can be used in commercial products without violating copyright or requiring attribution. Proprietary models may impose restrictions on output usage, while open-source models (e.g., CodeLlama, StarCoder) offer more flexibility for enterprise deployment. This is critical for compliance and intellectual property management in production environments.

Answer

Model's support for multiple programming languages.

Answer

Model's training data recency.

Answer

Model's parameter count (size).

Question 4

What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

Accepted Answer

They compress text into dense vectors for efficient retrieval.. Option A is correct because embedding models convert text into dense vector representations that capture semantic meaning, enabling efficient similarity search in vector databases. This compression reduces the dimensionality of the data, allowing the RAG system to quickly retrieve the most relevant documents from a large corpus based on vector distance metrics like cosine similarity.

Answer

They allow the model to generate new training data automatically.

Answer

They provide deterministic outputs for the same query.

Question 5

A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Accepted Answer

Enable the 'block offensive content' flag in the model's safety configuration.. Option D is correct because enabling the 'block offensive content' flag directly activates Gemini's built-in safety filters, which are designed to detect and suppress toxic language at the model's output layer. This is an immediate, configuration-level measure that requires no additional training or external services, making it the fastest way to reduce harmful responses in a production chatbot.

Answer

Increase the model's temperature to make outputs more conservative.

Answer

Use a separate language model to rephrase the outputs before sending to users.

Answer

Fine-tune the model on a curated dataset of polite conversations.

Question 6

Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?

Accepted Answer

The model was imported from a custom container but without a serving specification or artifact.. Option B is correct because a model imported from a custom container must include a serving specification (e.g., a `predict` route) and an artifact (e.g., a saved model file) to be deployable. Without these, Vertex AI cannot determine how to serve predictions, resulting in the 'Model is not deployable' error. The `gcloud` command listing the model only confirms its registration, not its readiness for deployment.

Answer

The model is still in training and not yet ready.

Answer

The model does not have the correct IAM permissions assigned to the deployment service account.

Answer

The region for the endpoint is different from the model's region.

Question 7

Refer to the exhibit. This IAM policy is applied to a Vertex AI project. A user 'test@example.com' reports they cannot create a ModelEvaluationPipelineJob. Which action should the administrator take?

Accepted Answer

Add the user to the roles/aiplatform.admin role at the project level.. Roles/aiplatform.user does not have the permissions to create pipeline jobs; it only allows viewing and using models and endpoints. Roles/aiplatform.admin has full control, so adding the user to this role is the simplest fix. There is no roles/aiplatform.specialist; removing the service account would not help; and granting at the model level is insufficient for creating pipeline jobs.

Answer

Grant the user roles/aiplatform.specialist at the project level.

Answer

Add the user roles/aiplatform.user at the model level to allow pipeline creation.

Answer

Remove the service account from roles/aiplatform.admin.

Question 8

Refer to the exhibit. A developer sees this error when trying to call a Vertex AI endpoint for online prediction. What permission does the requesting identity need to be granted?

Accepted Answer

aiplatform.endpoints.predict. The error occurs when calling a Vertex AI endpoint for online prediction, which requires the `aiplatform.endpoints.predict` permission. This permission is specifically scoped to the endpoint resource, allowing the identity to send prediction requests to a deployed model endpoint. The correct IAM role binding must include this permission for the requesting identity to successfully invoke the endpoint.

Answer

aiplatform.prediction.predict

Answer

aiplatform.endpoints.use

Answer

aiplatform.models.predict

Question 9

A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio provides access to pre-trained foundation models like Gemini for text generation via a user interface and API, making it the easiest choice for generating product descriptions without training.

Answer

Vertex AI Prediction

Answer

AI Platform Training

Answer

Cloud AutoML

Question 10

A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

Accepted Answer

Insufficient training data. Fine-tuning a large language model on Vertex AI with poor validation results is most likely due to insufficient training data. Large language models have billions of parameters and require a substantial amount of high-quality, task-specific data to effectively adapt to a new domain or task; without enough examples, the model cannot learn the desired patterns and will perform poorly on unseen data.

Answer

Incorrect learning rate

Answer

Using wrong model family

Answer

Overfitting due to too many epochs

Question 11

A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?

Accepted Answer

Increasing model temperature. Increasing model temperature makes the model more random and creative, which directly increases the likelihood of hallucinations. It does not constrain or ground the output in factual data, making it the least effective mitigation strategy among the options.

Answer

Using prompt templates with constraints

Answer

Using grounding with a knowledge base

Answer

Implementing retrieval-augmented generation

Question 12

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

Answer

To improve training speed

Answer

To reduce model size for faster inference

Answer

To enable multi-modal inputs

Question 13

A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

Accepted Answer

Use a prompt with the documents in the context. Option D is correct because Retrieval-Augmented Generation (RAG) allows the chatbot to dynamically include relevant internal documents in the prompt context without modifying the underlying model. This approach leverages the pre-trained model's language understanding while grounding answers in specific, up-to-date internal data, avoiding the cost and latency of fine-tuning or retraining.

Answer

Use a pre-trained model without any customizations

Answer

Train a custom model from scratch

Answer

Fine-tune a model on the documents

Question 14

A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Accepted Answer

temperature. Temperature controls the randomness of token selection during generation. A high temperature increases the likelihood of less probable tokens, which can lead to syntactically incorrect code. Lowering temperature makes the model more deterministic and conservative, favoring higher-probability tokens that are more likely to form valid syntax.

Answer

candidate_count

Answer

max_output_tokens

Answer

top_k

Question 15

Which Google Cloud product provides access to pre-trained foundation models like Gemini?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is the Google Cloud service specifically designed to provide access to pre-trained foundation models like Gemini, allowing users to test, customize, and deploy them via a managed interface. Unlike other services, Generative AI Studio directly integrates with Gemini's API and offers prompt engineering, tuning, and model evaluation capabilities.

Answer

Dataflow

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Cloud Translation

Answer

Vertex AI Model Registry