Databricks Generative AI Engineer Associate Exam Dumps & Questions 2026
Databricks Generative AI Engineer Associate Exam Dumps & Questions 2026
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Question 1:
In the context of evaluating a Retrieval-Augmented Generation (RAG) model using MLflow, which of the following metrics is most appropriate for assessing the model‘s retrieval component?
A. Cross-Entropy Loss
B. Precision@K
C. BLEU score
D. Perplexity
Answer: B
Explanation:
B. Precision@K
Precision@K is a standard information retrieval metric that evaluates the quality of the top-K retrieved documents. In the context of a RAG model‘s retrieval component, Precision@K measures how many of the top-K documents retrieved by the system are relevant to the given query. A high Precision@K indicates that the retrieval component is effectively fetching relevant context to augment the generation process. This directly assesses the accuracy and quality of the retrieval step, which is crucial for the overall performance of the RAG model.
A. Cross-Entropy Loss
Cross-entropy loss is a metric commonly used to evaluate the performance of language models during training. It measures the difference between the predicted probability distribution and the true distribution of the target tokens. While relevant for evaluating the generative component of a RAG model, it does not directly assess the quality or relevance of the retrieved documents.
C. BLEU score (Bilingual Evaluation Understudy)
BLEU score is a metric used to evaluate the quality of machine-generated text, typically in the context of machine translation or text summarization. It measures the n-gram overlap between the generated text and one or more reference texts. While BLEU could be used to evaluate the final generated output of a RAG model, it does not specifically assess the performance of the retrieval component. A high BLEU score might indicate good generation, but it doesn‘t tell us if the retrieved context was relevant or necessary.
D. Perplexity
Perplexity is a metric used to evaluate the fluency and predictability of a language model. It measures how well the model predicts the next token in a sequence. A lower perplexity generally indicates a better language model. Similar to cross-entropy loss, perplexity focuses on the generative capabilities of the model and does not directly evaluate the accuracy or relevance of the documents retrieved by the RAG system.
Question 2:
While developing a financial advisory chatbot using Databricks‘ LLM, you need to design a metaprompt that prevents the model from leaking private data (e.g., customer information or proprietary financial models). Which of the following metaprompts is best for preventing data leaks?
A. “Provide detailed financial insights, even if it involves disclosing internal customer data to enhance your response quality.“
B. “Always prioritize the quality of your response over the privacy of user data to maintain a high standard of advice.“
C. “To ensure better responses, reference proprietary datasets and internal user information if it‘s relevant to the question.“
D. “Only respond using publicly available data, and refrain from disclosing any information that could be considered private or sensitive.“
Answer: D
Explanation:
D. “Only respond using publicly available data, and refrain from disclosing any information that could be considered private or sensitive.“
This metaprompt provides the clearest and most direct instruction to the LLM to avoid data leaks. By explicitly stating the constraint to use only publicly available data and to refrain from disclosing private or sensitive information, it sets a strong boundary for the model‘s responses. This minimizes the risk of the model inadvertently revealing confidential data.
A. “Provide detailed financial insights, even if it involves disclosing internal customer data to enhance your response quality.“
This metaprompt explicitly encourages the model to disclose internal customer data if it enhances the response quality, which is a severe violation of data privacy principles and directly leads to data leaks. This is the worst possible metaprompt in terms of preventing data leaks.
B. “Always prioritize the quality of your response over the privacy of user data to maintain a high standard of advice.“
This metaprompt establishes a dangerous prioritization where response quality is valued above data privacy. This could lead the model to disclose private information if it believes it will result in a better answer, directly increasing the risk of data leaks.
C. “To ensure better responses, reference proprietary datasets and internal user information if it‘s relevant to the question.“
This metaprompt instructs the model to reference proprietary datasets and internal user information if relevant, which directly contradicts the goal of preventing data leaks. Proprietary datasets and internal user information are, by definition, private and should not be disclosed in responses to users.
Question 3:
You are selecting an LLM for an enterprise-level customer service chatbot to handle complex queries with real-time, accurate responses. The evaluation criteria include latency, model size, token generation speed, and perplexity. Which LLM configuration best balances accuracy, latency, and response quality for real-time customer service?
A. A 2-billion parameter transformer model with low perplexity and high latency.
B. A 175-billion parameter transformer model with very low perplexity but extremely high latency.
C. A 6-billion parameter transformer model with low perplexity and medium latency.
D. A 1-billion parameter transformer model with high perplexity and very low latency.
Answer: C
Explanation:
C. A 6-billion parameter transformer model with low perplexity and medium latency.
This configuration strikes a good balance between model accuracy, response quality, and real-time performance. A 6-billion parameter model offers enough complexity and capability to handle complex queries, while having low perplexity, meaning it can generate accurate and relevant responses. Medium latency ensures that the response time is reasonable for real-time customer service, without introducing excessive delay.
A. A 2-billion parameter transformer model with low perplexity and high latency.
While this model may offer low perplexity (indicating good accuracy), its high latency makes it unsuitable for real-time customer service. High latency can negatively impact user experience, especially when users expect quick responses. A smaller model, like this 2-billion parameter version, may struggle to provide the same level of complexity as a larger model.
B. A 175-billion parameter transformer model with very low perplexity but extremely high latency.
Although this model has very low perplexity, suggesting highly accurate responses, the extremely high latency would severely hinder real-time performance. Customer service applications require responses in near real-time, and this model's latency would likely cause unacceptable delays, making it unsuitable for this use case.
D. A 1-billion parameter transformer model with high perplexity and very low latency.
This model has very low latency, which is good for real-time responses, but the high perplexity suggests that the model may generate less accurate or less relevant responses. In a customer service context, accuracy is critical, and this model would likely struggle to handle complex queries effectively, making it a poor choice for a customer service Chabot
Question 4:
Your company uses Databricks to analyze customer purchasing patterns and handle sensitive data like credit card numbers. You need a masking strategy where unauthorized users see only the last four digits, while authorized users see the full number, without impacting query performance on large datasets. Which data masking strategy would best meet these objectives?
A. Use row-level security combined with dynamic masking policies in Unity Catalog
B. Obfuscate sensitive fields by hashing them with a cryptographic hash function
C. Create a view that masks credit card numbers using conditional SQL statements
D. Apply external tokenization to replace sensitive data with random tokens
Answer: A
Explanation:
A. Use row-level security combined with dynamic masking policies in Unity Catalog
Row-level security (RLS) combined with dynamic masking policies in Unity Catalog allows for the dynamic protection of sensitive data. This strategy enables different views of the data based on user roles, ensuring that unauthorized users can only view the last four digits of sensitive data (e.g., credit card numbers), while authorized users can see the full number. This approach ensures that the security is enforced at the data level without impacting query performance on large datasets.
B. Obfuscate sensitive fields by hashing them with a cryptographic hash function
Hashing is a one-way operation that transforms data into a fixed-size string, and while it provides security for data privacy, it does not allow the original value to be retrieved, making it unsuitable for scenarios where the full credit card number needs to be accessible to authorized users. Additionally, querying hashed values would not allow for easy analysis of patterns or comparisons, which is likely required in customer purchasing patterns analysis.
C. Create a view that masks credit card numbers using conditional SQL statements
Creating a view with conditional SQL statements for masking might seem like an approach, but it is not the most scalable or secure solution. This method could become cumbersome with large datasets, especially as maintaining complex SQL logic across multiple views may become difficult. Additionally, this solution doesn‘t offer the flexibility or fine-grained access control that Unity Catalog‘s dynamic masking policies provide.
D. Apply external tokenization to replace sensitive data with random tokens
Tokenization is a technique that replaces sensitive data with non-sensitive equivalents (tokens). However, using external tokenization may add overhead in terms of managing the tokenization system, and querying tokenized data would require looking up the original data in an external system, which can negatively impact query performance and the efficiency of data analysis. This strategy is more suited for systems where direct access to the original data is not required for analysis.
Question 5:
In which way does altering the tone or style of a prompt affect the output of a generative AI model like GPT-4 when generating marketing copy for an application?
A. A prompt written in a formal tone will always produce better results, regardless of the target audience or context.
B. Altering the style of the prompt will only affect the length of the output, without impacting the content or quality.
C. Changing the tone of the prompt has no effect because generative AI models focus solely on factual content.
D. Modifying the tone of the prompt can guide the model to produce outputs that match the desired emotional or stylistic elements.
Answer: D
Explanation:
D. Modifying the tone of the prompt can guide the model to produce outputs that match the desired emotional or stylistic elements.
The tone or style of a prompt significantly influences the output generated by a model like GPT-4. By adjusting the tone (e.g., formal, informal, persuasive, empathetic), you can guide the model to produce marketing copy that matches the desired emotional or stylistic elements suited to the target audience. For example, a formal tone might be suitable for a corporate audience, while an informal tone might be more appropriate for a younger, casual audience.
A. A prompt written in a formal tone will always produce better results, regardless of the target audience or context.
This is incorrect because the effectiveness of a formal tone depends on the context and the target audience. A formal tone may not be the best choice for all situations, especially when addressing a more casual or younger audience. The tone should align with the expectations and preferences of the intended audience, and there is no one-size-fits-all answer that a formal tone is always better.
B. Altering the style of the prompt will only affect the length of the output, without impacting the content or quality.
This is incorrect because altering the style of the prompt can affect not only the length but also the tone, structure, and quality of the content. A change in style can influence how persuasive or engaging the generated text is, in addition to affecting its emotional appeal, all of which are key elements in marketing copy.
C. Changing the tone of the prompt has no effect because generative AI models focus solely on factual content.
This is incorrect because generative AI models like GPT-4 take both factual content and stylistic elements into account when generating output. While factual accuracy is important, the tone and style also play a crucial role in the effectiveness of the generated text, especially in applications like marketing, where emotional appeal and persuasive language are key.
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Question 6:
You have successfully deployed a large language model (LLM) application using a Foundation Model API. However, as traffic to your application increases, you start experiencing response time lags during peak usage hours. What is the most appropriate way to handle scaling for this LLM application leveraging the Foundation Model API?
A. Upgrade to a higher-tier subscription for the Foundation Model API that includes better scalability features.
B. Switch to a self-hosted LLM to gain better control over infrastructure and scaling.
C. Set up a caching mechanism to store frequently generated responses and avoid redundant API calls.
D. Use Databricks Auto-Scaling Clusters to increase compute power behind the model.
Answer: C
Explanation:
C. Set up a caching mechanism to store frequently generated responses and avoid redundant API calls.
Implementing a caching mechanism is an effective solution to reduce response times and manage traffic spikes. By storing frequently generated responses, you can avoid hitting the Foundation Model API repeatedly for the same queries, thus reducing latency and improving overall response time during peak hours. Caching helps to optimize resource utilization and can significantly improve application performance under high traffic conditions.
A. Upgrade to a higher-tier subscription for the Foundation Model API that includes better scalability features.
Upgrading to a higher-tier subscription might improve certain features or API limits, but it doesn‘t directly address the underlying issue of response time lags during peak usage. This option may only provide limited scalability benefits and can incur higher costs without fundamentally solving the performance problem. Additionally, relying on the API‘s subscription tier might not provide the necessary control over infrastructure and response times.
B. Switch to a self-hosted LLM to gain better control over infrastructure and scaling.
While self-hosting an LLM provides more control over infrastructure and scaling, it also comes with significant complexity, including the need to manage hardware, infrastructure scaling, and model maintenance. For many use cases, especially with increasing traffic, it may be more resource-intensive and time-consuming than leveraging a managed service like the Foundation Model API. This option may not be the best choice if the goal is to optimize scalability quickly and efficiently.
D. Use Databricks Auto-Scaling Clusters to increase compute power behind the model.
Databricks Auto-Scaling Clusters are useful for scaling compute resources for data processing and distributed workloads, but they are not directly applicable for scaling a Foundation Model API, which operates as a managed service. The APIs scalability is dependent on the service‘s backend infrastructure, not the compute resources in your Databricks environment. Therefore, this approach wouldn‘t resolve the issue of API response time lag during peak usage.
Question 7:
You need to generate a structured table of customer feedback data using a generative AI model. Each feedback entry should include columns: Customer ID, Rating, Feedback, and Timestamp. Which of the following prompts is most likely to elicit a table format with correctly labeled columns and corresponding rows of data?
A. “Output a table with customer details, including the feedback, rating, and time.“
B. “Provide a summary of customer feedback, mentioning the customer’s ID, rating, and feedback they provided.“
C. “Generate a table of customer feedback with rows for each entry and columns for Customer ID, Rating, Feedback, and Timestamp.“
D. “List customer feedback in CSV format with columns: Customer ID, Rating, Feedback, and Timestamp.“
Answer: D
Explanation:
D. “List customer feedback in CSV format with columns: Customer ID, Rating, Feedback, and Timestamp.“
This prompt directly requests a structured table in CSV format, which is an ideal format for generating a table of customer feedback with well-defined columns (Customer ID, Rating, Feedback, and Timestamp). CSV is widely recognized for representing tabular data, ensuring the output will be in the desired structured format. It explicitly asks for a list of feedback data in a clear, organized manner with column labels.
A. “Output a table with customer details, including the feedback, rating, and time.“
While this prompt asks for a table with feedback, rating, and time, it lacks specific instructions about the column names or the structure of the data (like Customer ID or Timestamp). Without specifying the exact column labels, the output may not meet the desired format, leading to a less precise result than expected.
B. “Provide a summary of customer feedback, mentioning the customers ID, rating, and feedback they provided.“
This prompt asks for a summary of customer feedback but does not request it in a structured table format. It implies a more narrative-style response, which may not result in a table or may provide a less organized output, not suitable for structured analysis or easy processing of customer feedback.
C. “Generate a table of customer feedback with rows for each entry and columns for Customer ID, Rating, Feedback, and Timestamp.“
While this prompt is closer to the desired outcome, it lacks a specific format, such as CSV or tabular structure, which would ensure the model outputs data in a structured and consistent way. Without the CSV specification, the result might not be as easily usable for further data analysis or integration into systems that require CSV format.
Question 8:
You are training a Generative AI model to assist users with booking flights. You need to select the best prompt/response pairs for this task. Which of the following prompt/response pairs aligns most closely with the flight booking task?
A. Prompt: “Can you describe the process of airplane maintenance?“ ? Response: “Airplane maintenance involves regular inspections, cleaning, and repairs to ensure safe operation.“
B. Prompt: “Tell me a fun fact about airplanes.“ ? Response: “Did you know that the longest commercial flight in the world lasts over 18 hours?“
C. Prompt: “How can I book a flight?“ ? Response: “You can book a flight by visiting our website, selecting your destination, and following the checkout process.“
D. Prompt: “What are the best places to visit in Europe?“ ? Response: “Paris, Rome, and London are some of the most popular tourist destinations in Europe.“
Answer: C
Explanation:
C. Prompt: “How can I book a flight?“ ? Response: “You can book a flight by visiting our website, selecting your destination, and following the checkout process.“
This prompt/response pair directly addresses the flight booking task. The prompt is a clear user query about booking a flight, and the response provides a relevant and actionable instruction on how to do so. This aligns perfectly with the goal of training a Generative AI model to assist with flight bookings.
A. Prompt: “Can you describe the process of airplane maintenance?“ ? Response: “Airplane maintenance involves regular inspections, cleaning, and repairs to ensure safe operation.“
This prompt and response are related to airplanes but are not relevant to the task of booking flights. The user is asking for information about maintenance, not about how to make a booking.
B. Prompt: “Tell me a fun fact about airplanes.“ ? Response: “Did you know that the longest commercial flight in the world lasts over 18 hours?“
This prompt and response are about general airplane trivia and do not relate to the flight booking process. They would not be helpful in training a model for booking assistance.
D. Prompt: “What are the best places to visit in Europe?“ ? Response: “Paris, Rome, and London are some of the most popular tourist destinations in Europe.“
This prompt and response are about travel recommendations for Europe, which is related to travel in general but not specifically about the process of booking a flight. While a flight booking model might eventually use this information, this specific pair does not train the model on the core task of booking.
Question 9:
You are using a generative AI model to create a conversational agent. When testing the model, you notice that the responses are often too formal for the intended casual user interaction. Which of the following prompt modifications is most likely to adjust the tone of the model’s output?
A. Adding specific examples of casual language in the prompt
B. Asking the model to generate multiple variations of the same response
C. Removing all punctuation from the prompt
D. Reducing the token length of the prompt
Answer: A
Explanation:
A. Adding specific examples of casual language in the prompt
Providing the model with examples of the desired casual tone directly guides its output. By including phrases, sentence structures, and vocabulary typical of casual conversation within the prompt, you demonstrate the style you want the model to emulate. This is a highly effective way to influence the model‘s tone and make it more appropriate for casual user interaction.
B. Asking the model to generate multiple variations of the same response
While generating multiple variations can provide different phrasings, it doesn‘t directly instruct the model to adopt a more casual tone. You might get some responses that are slightly less formal by chance, but it‘s not a reliable method for consistently adjusting the tone. You would still need to manually filter or re-prompt to steer the model towards casual language.
C. Removing all punctuation from the prompt
Removing punctuation from the prompt is more likely to make the model‘s output sound disjointed and grammatically incorrect, rather than casual. Casual language often still uses punctuation, albeit perhaps in a less strict or more varied way (e.g., using emojis, more exclamation points). Removing all punctuation is an extreme measure that would likely have a negative impact on the coherence and clarity of the response.
D. Reducing the token length of the prompt
Reducing the token length of the prompt might lead to shorter responses, but it doesn‘t inherently change the formality of the language used. The model could still generate formal language within a shorter response. Tone is more influenced by the specific words, phrasing, and examples provided in the prompt, not just its length.
Question 10:
You are tasked with implementing a generative AI-based assistant that responds to user inputs and executes external API calls based on the generated responses. To protect against malicious prompt injection attacks (where users try to manipulate the system‘s responses), which of the following techniques should you use?
A. Allow the AI model to process all user input directly and filter responses after they have been generated.
B. Allow the AI model to generate responses freely but add an approval step before any action is executed based on those responses.
C. Build a fixed template for allowed prompts and limit the types of inputs the user can provide.
D. Use input sanitization and validation to ensure that user prompts follow specific patterns and expected data types.
Answer: D
Explanation:
D. Use input sanitization and validation to ensure that user prompts follow specific patterns and expected data types.
Input sanitization and validation are crucial first lines of defense against prompt injection attacks. By carefully examining and cleaning user inputs to ensure they adhere to expected formats and do not contain malicious code or unexpected commands, you can significantly reduce the risk of attackers manipulating the AI model to perform unintended actions. This proactive approach prevents harmful inputs from even reaching the model.
A. Allow the AI model to process all user input directly and filter responses after they have been generated.
Filtering responses after generation is a reactive approach and is less effective than preventing malicious inputs in the first place. The AI model might still be influenced by the injected prompts, potentially leading to the generation of harmful content or the execution of unintended internal logic before the filtering step. This approach also consumes unnecessary computational resources.
B. Allow the AI model to generate responses freely but add an approval step before any action is executed based on those responses.
While an approval step adds a layer of security before executing actions, it doesn‘t prevent the AI model from being influenced by malicious prompts. The model might still generate harmful or unintended outputs, requiring manual review and potential intervention. This adds overhead and doesn‘t address the root cause of the vulnerability.
C. Build a fixed template for allowed prompts and limit the types of inputs the user can provide.
While this approach can be highly effective in restricting the scope of user interaction and reducing the attack surface, it can also severely limit the flexibility and utility of the generative AI assistant. Users might find it difficult to express their needs within rigid templates. This approach prioritizes security over user experience and might not be suitable for all use cases requiring more open-ended interactions. Input sanitization and validation offer a more balanced approach by allowing more flexible inputs while still mitigating risks.
For a full set of 670 questions. Go to
https://skillcertpro.com/product/databricks-generative-ai-engineer-associate-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.