AIF-C01 Practice Test 33 — 15 Questions

Question 1

A financial services company is deploying a machine learning model that must comply with SOC 2 and PCI DSS. They need to ensure that the model artifacts and training data are encrypted, access is audited, and the environment is protected from network threats. Which THREE AWS services should they implement?

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

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior, which helps protect the environment from network threats as required by SOC 2 and PCI DSS. By analyzing VPC Flow Logs, DNS logs, and CloudTrail events, it can detect anomalies such as port scanning or data exfiltration, directly addressing the need for network threat protection in a compliant ML deployment.

Answer

Amazon DynamoDB Accelerator (DAX)

Answer

AWS WAF

Question 2

During model training, the loss decreases rapidly for the first few epochs and then plateaus. The validation loss starts increasing after some epochs. What should the team do to improve generalization?

Accepted Answer

Early stopping. The validation loss increasing while training loss continues to decrease is a classic sign of overfitting. Early stopping (Option B) halts training when validation performance stops improving, preventing the model from memorizing noise in the training data and thereby improving generalization.

Answer

Increase learning rate

Answer

Increase training epochs

Answer

Add more layers

Question 3

An organization uses Amazon Bedrock to generate content. They have implemented guardrails to block toxic content. However, some users are able to bypass the guardrails by encoding their prompts. What step should be taken to improve security?

Accepted Answer

Enable prompt injection detection in the guardrail configuration.. Amazon Bedrock guardrails include a built-in prompt injection detection capability that can identify and block attempts to bypass content filters through encoded or obfuscated prompts. Enabling this feature specifically addresses the scenario where users encode their inputs to evade toxic content blocking, as it analyzes the decoded intent of the prompt rather than just the surface-level encoding.

Answer

Encode the prompts before sending to the model.

Answer

Use a different foundation model that is less susceptible.

Answer

Restrict access to the model using IAM policies.

Question 4

A financial services company is deploying a machine learning model that must comply with SOC 2 and PCI DSS. They need to ensure that the model artifacts and training data are encrypted, access is audited, and the environment is protected from network threats. Which THREE AWS services should they implement?

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

Accepted Answer

Denied topics. To prevent PII leakage, enable PII redaction. To restrict to product documentation, use topic restrictions (denied topics for off-topic). To prevent harmful content, use content filters. Word filters are for specific words, and grounding check is for hallucination prevention but not directly for topic restriction.

Answer

Word filters

Answer

Grounding check

Question 5

A developer is using Amazon Bedrock to generate product descriptions. The developer notices that the model sometimes outputs descriptions that contradict the provided product specifications. Which parameter adjustment would MOST directly reduce factual inconsistencies?

Accepted Answer

Decrease temperature to a value close to 0. Decreasing temperature to a value close to 0 makes the model more deterministic and less creative, which reduces the likelihood of generating random or contradictory content. In Amazon Bedrock, temperature controls the randomness of token selection; lower values cause the model to choose the most probable tokens, aligning outputs more closely with the provided product specifications and minimizing factual inconsistencies.

Answer

Increase maxTokens to allow longer descriptions

Answer

Increase topP to 1.0

Answer

Set topK to a higher value

Question 6

A healthcare company needs to use Amazon SageMaker Ground Truth for data labeling. The data includes protected health information (PHI) that must remain in the US. Which configuration meets the compliance requirements?

Accepted Answer

Use a private workforce consisting of the company's employees and launch the labeling job in the us-east-1 region. A private workforce consisting of the company's own employees ensures that PHI is never exposed to external workers, and launching the labeling job in us-east-1 keeps all data within the US, satisfying the data residency requirement. Amazon SageMaker Ground Truth allows you to restrict data access to a private workforce that you manage, and by selecting a US region, you ensure that data processing and storage remain within US borders.

Answer

Use a vendor-managed workforce and set up data encryption

Answer

Use a public workforce (Mechanical Turk) and select the US East region

Answer

Use a private workforce but launch the labeling job in the eu-west-1 region

Question 7

A financial services company uses Amazon Bedrock Knowledge Bases to power a Q&A bot for analysts. They notice that the bot sometimes gives outdated information because documents are updated weekly. They cannot retrain or rebuild the knowledge base weekly. What is the MOST efficient solution?

Accepted Answer

Use incremental data ingestion to sync only the updated documents weekly. Amazon Bedrock Knowledge Bases supports incremental data ingestion, which allows you to sync only the documents that have changed since the last sync. This avoids the need to rebuild the entire knowledge base from scratch, saving time and compute resources while keeping the Q&A bot up to date with weekly document updates.

Answer

Rebuild the entire knowledge base from scratch every week using a scheduled job

Answer

Set the chunk size to zero so the entire document is always retrieved

Answer

Use a Lambda function to manually delete and re‑upload all documents each week

Question 8

Which TWO AWS services can be used to monitor and detect security anomalies in Amazon SageMaker model inference data? (Choose TWO.)

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is specifically designed to detect deviations in model quality, such as data drift and feature attribution drift, by continuously monitoring inference data against a baseline. Amazon CloudWatch Logs can be used to capture and analyze inference request logs, enabling custom anomaly detection through log-based metrics and alarms. Together, they provide a comprehensive approach to monitoring security anomalies in SageMaker model inference data.

Answer

Amazon Macie

Answer

AWS CloudTrail

Answer

Amazon CodeGuru Security

Question 9

A machine learning engineer wants to ensure that a SageMaker notebook instance only has access to a specific S3 bucket containing training data. The notebook instance is in a VPC. What is the most secure way to restrict access?

Accepted Answer

Use a VPC endpoint for S3 and a bucket policy that restricts access to the VPC endpoint.. Using a VPC endpoint for S3 combined with a bucket policy that restricts access to that specific endpoint ensures that only traffic originating from within the VPC (and thus from the SageMaker notebook instance) can reach the S3 bucket. This approach enforces network-level isolation and prevents access from any other source, including the public internet, even if the IAM role is compromised. It is the most secure method because it layers network policy (VPC endpoint) with resource-based policy (bucket policy) to create a tightly scoped access control.

Answer

Place the notebook instance in a private subnet with a NAT gateway.

Answer

Use AWS KMS to encrypt the bucket and grant the notebook role decrypt permissions.

Answer

Assign an IAM role to the notebook with an S3 bucket policy that only allows access to that bucket.

Question 10

A company is building a generative AI application using Amazon Bedrock and needs to ensure that the model does not generate outputs containing personally identifiable information (PII). Which TWO actions should the company take? (Choose 2)

Accepted Answer

Implement a custom AWS Lambda function to scan and redact PII from inputs and outputs.. A custom AWS Lambda function can be integrated into the application workflow to programmatically scan and redact PII from both inputs and outputs before they reach or leave the Bedrock model. This provides a flexible, code-driven approach to data sanitization, allowing the use of libraries like Amazon Comprehend or regex patterns to detect and mask PII entities such as names, addresses, and social security numbers.

Answer

Use AWS Identity and Access Management (IAM) policies to restrict model access.

Answer

Enable Amazon CloudWatch Logs to capture and audit model outputs.

Answer

Place the Bedrock model endpoint within a private VPC.

Question 11

A company wants to use a large language model to generate code based on natural language descriptions. They need to minimize latency and control costs by running inference on their own infrastructure. Which approach is most suitable?

Accepted Answer

Use Amazon SageMaker to deploy a custom LLM. Amazon SageMaker allows you to deploy a custom large language model (LLM) on your own infrastructure, giving you full control over inference latency and cost. By using SageMaker endpoints with auto-scaling and instance selection, you can optimize for low-latency responses while avoiding per-token API charges from managed services.

Answer

Use Amazon Bedrock API

Answer

Use Amazon Comprehend

Answer

Use Amazon Lex

Question 12

Refer to the exhibit. A data scientist runs an Amazon SageMaker Clarify bias analysis on a binary classifier. The pre-training ClassImbalance is 1.5 and the post-training DPPL is 0.15. What should the data scientist conclude?

Accepted Answer

The data has a mild class imbalance, but the model shows a noticeable bias in predictions.. The pre-training ClassImbalance metric of 1.5 indicates a mild class imbalance (values close to 1.0 indicate balance, while values significantly above 1.0 indicate imbalance). The post-training DPPL (Difference in Positive Proportions in Labels) metric of 0.15 exceeds the commonly accepted fairness threshold of 0.10, indicating a noticeable bias in the model's predictions. Therefore, the data has a mild imbalance, but the model exhibits a bias that warrants further investigation.

Answer

The data is highly imbalanced and the model is unbiased.

Answer

The pre-training metric indicates a fairness issue, but the post-training metric is acceptable.

Answer

The data is perfectly balanced and the model is fair.

Question 13

A company uses Amazon SageMaker Ground Truth to label a dataset for a binary classifier. To reduce labeling bias, which workforce configuration is most appropriate?

Accepted Answer

Private workforce of domain experts. A private workforce of domain experts ensures that labeling is performed by individuals with deep knowledge of the data domain, which directly reduces labeling bias. Domain experts are less likely to misinterpret ambiguous data points and can apply consistent, informed judgment, thereby minimizing systematic errors that could skew the binary classifier's training data.

Answer

Automatic labeling with Active Learning

Answer

Public workforce with no qualification

Answer

Vendor managed workforce

Question 14

A company is using Amazon Bedrock to generate product descriptions. They notice that the model sometimes produces descriptions that contain factual errors about the products. Which TWO actions should they take to improve factual accuracy?

Accepted Answer

Implement Retrieval Augmented Generation (RAG) with a product knowledge base. Retrieval Augmented Generation (RAG) grounds the model's output in a curated product knowledge base, allowing it to retrieve and cite authoritative facts during generation. This directly reduces hallucinations by ensuring the model references verified data rather than relying solely on its parametric memory.

Answer

Reduce the temperature parameter to 0.1

Answer

Increase the max_tokens to allow longer descriptions

Answer

Use human reviewers to correct errors after generation

Question 15

A team is using a prompt engineering technique where they provide a few examples of desired input-output pairs in the prompt to guide the model's response. Which technique are they using?

Accepted Answer

Few-shot prompting. Few-shot prompting (Option C) is the correct technique because it involves providing a small number of input-output examples within the prompt to condition the model on the desired task format and pattern. This approach helps the model generalize from the examples to produce accurate responses for new inputs, without requiring fine-tuning.

Answer

System prompting

Answer

Zero-shot prompting

Answer

Chain-of-thought prompting