AIF-C01 Practice Test 32 — 15 Questions

Question 1

A data scientist trains a binary classifier and obtains the following confusion matrix on the test set: TP=120, FN=30, FP=40, TN=210. What is the F1 score?

Accepted Answer

0.774. The F1 score is the harmonic mean of precision and recall. Precision = TP/(TP+FP) = 120/160 = 0.75, and recall = TP/(TP+FN) = 120/150 = 0.80. F1 = 2 * (precision * recall) / (precision + recall) = 2 * (0.75 * 0.80) / (0.75 + 0.80) = 2 * 0.60 / 1.55 = 1.20 / 1.55 ≈ 0.774. Option B is correct.

Answer

0.800

Answer

0.750

Answer

0.823

Question 2

A retail company is deploying a machine learning model to analyze customer reviews and predict sentiment. The team wants to follow responsible AI guidelines to ensure fairness, transparency, and accountability. Which TWO actions should the team take? (Choose TWO.)

Accepted Answer

Use SageMaker Clarify to evaluate bias in the training data.. Option B is correct because SageMaker Clarify is the AWS service specifically designed to detect potential bias in training data and models, providing bias metrics (such as class imbalance and disparate impact) that directly support the fairness pillar of responsible AI. Option E is correct because SageMaker Model Cards provide a structured way to document a model's intended use, limitations, evaluation results, and risk information, which directly supports transparency and accountability requirements. Option A is not correct because SageMaker Debugger focuses on training performance and convergence issues (tensor analysis, profiling), not on fairness, transparency, or accountability. Option C is not correct because SageMaker Model Monitor detects data and model drift and can trigger retraining, but it addresses operational model quality rather than the responsible AI goals of fairness and transparency. Option D is not correct because Amazon Rekognition is a computer vision service for image and video analysis and cannot detect PII in review text; Amazon Comprehend would be the appropriate text-based service.

Answer

Use SageMaker Debugger to optimize training performance.

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Use SageMaker Model Monitor to automatically retrain the model when drift is detected.

Answer

Use Amazon Rekognition to detect personally identifiable information (PII) in the review text.

Question 3

A team has created a knowledge base in Amazon Bedrock for a Q&A application. After updating the source documents, they notice that the model still returns old information. What is the MOST likely cause?

Accepted Answer

The knowledge base has not been resynchronized after updating the documents. When source documents in an Amazon Bedrock knowledge base are updated, the knowledge base must be resynchronized to ingest the changes. Without resynchronization, the vector embeddings and index still reflect the old documents, so the model continues to retrieve outdated information.

Answer

The chunking strategy is incorrect

Answer

The foundation model has a limited context window

Answer

The guardrails are filtering out the new information

Question 4

A company uses Amazon Bedrock to build a chatbot. The chatbot needs to answer questions based on internal company documents. Which AWS service should be integrated with Bedrock to enable Retrieval Augmented Generation (RAG) without managing infrastructure?

Accepted Answer

Amazon Kendra. Amazon Kendra is a fully managed intelligent search service that can be directly integrated with Amazon Bedrock to implement Retrieval Augmented Generation (RAG) without any infrastructure management. It indexes internal company documents and retrieves relevant passages, which are then passed to the foundation model as context to generate accurate, grounded answers.

Answer

Amazon OpenSearch Service

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Amazon DynamoDB

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Amazon RDS

Question 5

A data scientist is using Amazon Bedrock to generate product descriptions. They discover that the model frequently repeats phrases and produces overly deterministic outputs. Which parameter adjustment would MOST likely introduce more diversity?

Accepted Answer

Increase the temperature from 0.7 to 1.2. Increasing the temperature from 0.7 to 1.2 raises the randomness of token selection by flattening the probability distribution, which makes lower-probability tokens more likely to be chosen. This directly counters the overly deterministic and repetitive outputs by introducing more diversity into the generated text.

Answer

Increase the top_p value from 0.9 to 1.0

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Decrease the top_k value from 50 to 10

Answer

Decrease the temperature from 0.7 to 0.2

Question 6

A data scientist is using a pre-trained LLM for a text summarization task. They notice the model sometimes includes hallucinations (false information) in the summaries. Which THREE prompt engineering techniques can help reduce hallucinations? (Select THREE.)

Accepted Answer

Use few-shot prompting with examples that demonstrate accurate summaries. Option B is correct because few-shot prompting supplies the model with concrete input/output exemplars of accurate, grounded summaries, which conditions the model on the desired summarization pattern and reduces the tendency to fabricate content not present in the source. Option D is correct because chain-of-thought prompting makes the model generate intermediate reasoning steps, forcing it to derive the summary from the provided text rather than emitting plausible-sounding but unsupported statements, which lowers hallucination rates. Option E is correct because lowering the temperature toward 0.0 makes decoding more deterministic and favors the highest-probability tokens, reducing the sampling randomness that produces invented facts. Option A is not appropriate because increasing max tokens only permits longer output and does not address factual grounding, and can even give more room for hallucinated content. Option C is not appropriate because raising the temperature to 0.9 increases randomness and creativity, which typically worsens hallucinations rather than reducing them.

Answer

Increase the max tokens to allow longer summaries

Answer

Increase the temperature to 0.9 for more creative summaries

Question 7

Which component of the Transformer architecture allows the model to weigh the importance of different words in a sequence when generating output?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of positions in the input sequence, allowing the model to dynamically weigh the importance of different words when generating each output token. This enables the model to capture long-range dependencies and contextual relationships without the sequential processing limitations of RNNs.

Answer

Feed-forward neural network

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Positional encoding

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Layer normalization

Question 8

Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating output?

Accepted Answer

Self-attention mechanism. The self-attention mechanism (B) is the core component that enables the Transformer to dynamically assign importance weights to every token in the input sequence relative to every other token. This allows the model to capture long-range dependencies and contextual relationships, which is essential for generating coherent output in tasks like translation or summarization.

Answer

Layer normalization

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Positional encoding

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Feed-forward neural network

Question 9

A company needs to transcribe and summarize customer support calls in real time. They want to use a large language model (LLM) for summarization but the audio input is streaming. Which approach should they use?

Accepted Answer

Transcribe the audio using Amazon Transcribe and then pass the text to an LLM for summarization. Amazon Transcribe is designed to handle streaming audio input and convert it to text in real time, which can then be passed to an LLM for summarization. This two-step pipeline leverages the strengths of each service: Transcribe for accurate speech-to-text conversion and an LLM for natural language summarization, without requiring a multimodal model that directly processes audio.

Answer

Use a multimodal LLM that directly accepts audio input

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Use an image generation model to create a visual summary of the audio

Answer

Use an embedding model to convert audio directly into searchable vectors

Question 10

A data scientist is using Amazon Bedrock to generate product descriptions. The current prompt produces inconsistent results; sometimes the descriptions are too verbose, other times too short. The scientist wants to reduce output variability and set a consistent tone. Which combination of parameters should be adjusted?

Accepted Answer

Lower temperature to 0.1 and set top-p to 0.9. Setting a low temperature (0.1) makes the model more deterministic and less random, reducing output variability, while a high top-p (0.9) allows the model to consider a broad but controlled set of likely tokens, ensuring consistent tone without being overly restrictive. This combination balances creativity and consistency, directly addressing the problem of inconsistent verbosity.

Answer

Set temperature to 0 and top-p to 0.5

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Increase max tokens to 500 and set stop sequences

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Increase temperature to 0.9 and set top-p to 1.0

Question 11

A developer is building a customer-facing chatbot using Amazon Bedrock. To ensure the chatbot does not generate offensive or inappropriate content, which AWS feature should they implement?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it provides configurable safeguards that allow developers to define denied topics, content filters (e.g., hate, insults, sexual content), and sensitive information filters to prevent the model from generating offensive or inappropriate responses. Unlike prompt engineering or parameter tuning, Guardrails enforce policy-based constraints at inference time, independent of the underlying model's behavior.

Answer

AWS Identity and Access Management (IAM) policies

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Prompt engineering with system prompts

Answer

Increasing the model temperature parameter

Question 12

A retail company uses Amazon Bedrock with Anthropic Claude to generate personalized marketing emails. They want to include dynamic content such as the customer's name and recent purchase history. Which API should they use to enable multi-turn conversations with context management?

Accepted Answer

Converse API. The Converse API (option B) is the correct choice because it is specifically designed for multi-turn conversations with context management in Amazon Bedrock. It automatically handles conversation history, allowing the model to maintain context across multiple exchanges, which is essential for generating personalized marketing emails that reference dynamic content like customer names and purchase history from previous interactions.

Answer

Amazon Comprehend API

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InvokeModel API

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Amazon Lex API

Question 13

An insurance company uses a machine learning model to adjust premiums. During a review, the model is found to be penalizing customers based on zip codes correlated with racial demographics, leading to potential discrimination. Which combination of actions best addresses this fairness issue while maintaining business value?

Accepted Answer

Re-engineer features to avoid proxies for protected attributes and rebalance training data. Re-engineering features to remove proxies for protected attributes (e.g., zip codes correlated with race) directly addresses the root cause of bias without discarding all geographic information. Rebalancing the training data helps mitigate skewed representations that could amplify discriminatory patterns, preserving business value by retaining useful predictive signals while aligning with fairness principles under the AIF-C01 Responsible AI guidelines.

Answer

Remove the zip code feature from the model and retrain

Answer

Continue using the model but add a disclaimer about potential bias

Answer

Replace the model with a simpler linear model

Question 14

A data scientist is building a regression model to predict house prices. During feature engineering, they have categorical variables (e.g., neighborhood) and numerical variables (e.g., square footage) with missing values. Which TWO actions should the data scientist take? (Choose two.)

Accepted Answer

Impute missing square footage values with the median. Option C is correct because imputing missing square footage values with the median is a robust strategy for numerical features, especially when outliers may be present, and it preserves the row count for model training. Option E is correct because one-hot encoding is the appropriate technique for nominal categorical variables like neighborhood, as it creates binary columns without imposing an artificial ordinal relationship. Option A is not ideal because dropping all rows with any missing values can discard substantial useful data and introduce bias. Option B is not required for regression models and may not be necessary unless the algorithm is sensitive to feature scale. Option D is incorrect because label encoding would impose an arbitrary order on neighborhood categories, which is inappropriate for a nominal variable.

Answer

Drop all rows with any missing values

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Normalize all numerical features to [0,1] range

Answer

Apply label encoding to the neighborhood variable

Question 15

A company is using Amazon Bedrock to build an application that requires very low latency responses (under 100ms). They are currently using a large model but need faster inference. Which model selection strategy is MOST appropriate?

Accepted Answer

Switch to a smaller model that can meet the latency requirement while still providing acceptable quality. Smaller models have fewer parameters, which reduces the computational cost per inference, directly lowering latency. For latency-sensitive applications requiring under 100ms responses, a smaller model can often provide acceptable quality while meeting the strict timing requirement, whereas a larger model would introduce higher inference latency due to increased matrix operations and memory bandwidth demands.

Answer

Increase the temperature parameter to speed up generation

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Use a larger model with more parameters for better accuracy

Answer

Use batch processing instead of real-time streaming