Generative AI Leader Practice Test 6 — 10 Questions

Question 1

A tech company wants to ensure that their generative AI model does not produce harmful content. They plan to use Google Cloud's content safety features. Which two methods can they use to customize content safety? (Choose two.)

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

Accepted Answer

Adjust safety thresholds for different categories like hate speech and violence. Option C is correct because Google Cloud's generative AI safety features (such as those in Vertex AI) let you configure per-category safety thresholds, so you can raise or lower sensitivity for categories like hate speech, harassment, sexually explicit content, and violence to match your tolerance policy. Option D is correct because you can supply a custom blocklist of prohibited terms or phrases, which the service checks in addition to the built-in safety filters, giving you organization-specific control over banned content. Option A is not a customization method—using default filters unchanged is the absence of customization. Option B is wrong because disabling all safety filters removes protection rather than customizing it, and it is not a supported way to ensure harmful content is blocked. Option E is not part of Google Cloud's content safety customization features; training a separate detection model is a different, external approach rather than configuring the built-in safety controls.

Answer

Use the default safety filters without any modifications

Answer

Disable all safety filters for maximum model creativity

Answer

Train a separate model to detect harmful content

Question 2

A healthcare organization needs to run ML models on patient data stored in BigQuery while ensuring data never leaves the database. Which service allows them to create and execute ML models directly in BigQuery SQL?

Accepted Answer

BigQuery ML. BigQuery ML enables SQL-based ML model creation and execution directly on data in BigQuery, meeting the requirement without data movement.

Answer

Cloud Functions

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Question 3

An organization is running a large-scale training job for a custom NLP model with a batch size of 2048 and sequence length of 512. They need to minimize training time while keeping costs predictable. Which Google Cloud hardware should they choose?

Accepted Answer

Cloud TPU v5e pods. Cloud TPU v5e pods are purpose-built for large-scale training of transformer-based NLP models, offering high-throughput matrix multiplication and efficient scaling across multiple chips. With a batch size of 2048 and sequence length of 512, TPU v5e pods deliver superior training speed and predictable pricing via reserved capacity, minimizing time-to-train compared to GPU alternatives.

Answer

Compute Engine with NVIDIA A100 GPUs

Answer

Edge TPU devices

Answer

Compute Engine with NVIDIA T4 GPUs

Question 4

A media company uses a generative AI model to create marketing images. They want to ensure that AI-generated images can be identified as synthetic. Which Google Cloud capability should they use?

Accepted Answer

SynthID. SynthID is Google DeepMind's watermarking technology that embeds imperceptible digital watermarks into AI-generated content, including images, and provides a detector to identify them as synthetic. It is purpose-built for provenance and identification of generative AI output, matching the media company's requirement.

Answer

Cloud Vision API

Answer

Data Loss Prevention (DLP) API

Answer

Vertex AI Model Registry

Question 5

A team is fine-tuning a model for a legal document summarization task. They need to ensure high accuracy and avoid hallucinations. Which TWO approaches should they combine? (Choose two.)

Accepted Answer

Use Retrieval-Augmented Generation to retrieve relevant legal texts. Retrieval-Augmented Generation (RAG) is correct because it grounds the model's output in retrieved, authoritative legal texts, directly reducing hallucination by providing factual context during generation. This is critical for legal summarization where accuracy is paramount, as RAG ensures the model references specific statutes or case law rather than relying solely on its parametric memory.

Answer

Increase temperature to 1.5 during inference

Answer

Implement early stopping during fine-tuning

Answer

Use character-level tokenization to improve spelling

Question 6

In the transformer architecture, what is the role of the attention mechanism?

Accepted Answer

It decides which parts of the input to focus on when generating each token. The attention mechanism in the Transformer architecture computes a weighted sum of all input token representations, allowing the model to dynamically focus on the most relevant parts of the input sequence when generating each output token. This is achieved through learned query, key, and value projections that produce attention scores, enabling the model to capture long-range dependencies and contextual relationships. Option B correctly identifies this core function of selectively attending to input elements during token generation.

Answer

It normalizes the output of each layer

Answer

It predicts the next token directly

Answer

It converts tokens into numerical vectors

Question 7

An employee wants to use GenAI to assist with writing formulas in Google Sheets. Which Gemini for Google Workspace feature should they use?

Accepted Answer

Formula assistance in Google Sheets. Gemini for Sheets provides formula assistance, helping users generate, explain, or debug formulas.

Answer

Help me write in Google Docs

Answer

Image generation in Google Slides

Answer

Smart Compose in Gmail

Question 8

A data scientist is using Vertex AI RAG Engine to build a question-answering system over a large corpus of technical manuals. Users report that answers are often verbose and include irrelevant details. Which configuration change is MOST likely to improve answer conciseness?

Accepted Answer

Decrease the number of retrieved documents (k) in the retrieval step. In a RAG pipeline, the number of retrieved documents (k) directly controls how much context is fed to the LLM. Reducing k limits the amount of potentially irrelevant material the model sees, which typically makes answers shorter and more focused. This is the most direct lever for conciseness.

Answer

Increase the chunk size of documents in the index

Answer

Use a different embedding model for retrieval

Answer

Increase the max output token limit

Question 9

You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

Accepted Answer

Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5. Upgrading to a larger machine (n1-standard-8) provides more CPU cores to handle the increased inference workload, while enabling autoscaling (min=1, max=5) allows the deployment to dynamically add replicas during peak hours to distribute the load and reduce latency. This combination addresses the high CPU utilization without over-provisioning during off-peak times, thus controlling cost.

Answer

Optimize the model using quantization and reduce the number of replicas

Answer

Switch to batch prediction instead of online prediction

Answer

Add a GPU accelerator to the existing machine

Question 10

After deploying a text-to-image model, the output images often contain distorted objects. The team suspects the prompt is too complex. Which prompt engineering technique should they try first?

Accepted Answer

Break the prompt into simpler, separate steps.. Breaking a complex prompt into simpler, separate steps reduces the cognitive load on the diffusion model, allowing it to focus on generating each element sequentially. This technique, often called 'prompt decomposition' or 'step-by-step prompting,' directly addresses the root cause of distorted objects when the model struggles to attend to multiple conflicting details simultaneously in a single pass.

Answer

Increase the guidance scale.

Answer

Add more descriptive adjectives.

Answer

Use a negative prompt to exclude distortions.