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| Section | Objectives |
|---|---|
| Generative AI Concepts | - Generative models
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
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NEW QUESTION # 29
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?
Answer: C
Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.
NEW QUESTION # 30
What is the correct order of steps in an ML project?
Answer: B
Explanation:
The standard ML project lifecycle proceeds: data collection first, since you need raw data before anything else can happen; data preprocessing next, to clean, transform, and prepare that raw data (handling missing values, normalization, encoding, splitting into train/validation/test sets) into a form a model can consume; model training next, where the algorithm learns patterns from the preprocessed training data; and model evaluation last, where the trained model's performance is measured on held-out data it did not see during training. Each stage depends on the output of the one before it - you cannot preprocess data you haven't collected, train on data that hasn't been cleaned and split, or evaluate a model that hasn't been trained - which is what makes B the only internally consistent ordering among the four options.
Options A, C, and D each place a downstream step before its prerequisite: A attempts preprocessing before collection (nothing to preprocess yet); C and D both place evaluation before training and, in D's case, before data even exists - evaluation requires a trained model to assess, so it cannot logically precede training or the data-collection/preprocessing steps that training itself depends on.
In practice this pipeline is iterative rather than strictly linear - evaluation results often send you back to preprocessing (feature engineering) or even data collection (targeted collection to address weak subgroups) - but the canonical forward sequence for a first pass remains collection # preprocessing # training # evaluation.
Reference: Core Machine Learning and AI Knowledge domain - the standard ML project/pipeline lifecycle.
NEW QUESTION # 31
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
Answer: A
Explanation:
Bias evaluation for facial recognition centers on measuring whether the model's accuracy - true positive rate, false positive rate, false match rate - is consistent across demographic subgroups (race, gender, age), rather than assuming a single aggregate accuracy figure represents performance fairly for all groups. This concern is well grounded empirically: independent benchmarking, including NIST's Face Recognition Vendor Test studies, has repeatedly documented substantial accuracy disparities across demographic groups in widely deployed facial recognition systems, with error rates for some subgroups measured many times higher than for others - a direct consequence of training data underrepresentation and the representativeness-bias issue covered elsewhere in this domain.
Processing speed (A) is a performance/latency engineering concern, not a bias or fairness concern - a model could process all demographic groups at identical speed while still exhibiting severe accuracy disparities between them. Facial expression recognition capability (C) addresses a different task dimension (emotion
/expression classification) than the identity-recognition bias question being asked. Operating system compatibility (D) is a software-deployment/engineering concern entirely unrelated to model fairness.
Subgroup accuracy disparities in facial recognition carry serious real-world consequences - misidentification risk in law enforcement or access-control contexts - which is why disaggregated (per-subgroup) evaluation, not just aggregate accuracy, is considered a baseline requirement under Trustworthy AI bias auditing practice.
Reference: Trustworthy AI domain - subgroup/disaggregated bias evaluation, fairness auditing.
NEW QUESTION # 32
Explain the role of Tensor Cores and mixed-precision training (e.g., using FP16 or bfloat16) in accelerating the training of large generative AI models.
Answer: C
Explanation:
Tensor Cores are designed to accelerate matrix multiplication, the core operation in deep learning, using lower precision data types. Mixed-precision training leverages this by using lower precision for the bulk of the computation, while maintaining higher precision for critical variables to avoid instability. Tensor Cores are used both for training and inference.
NEW QUESTION # 33
You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)
Answer: B,D
Explanation:
More sophisticated attention mechanisms help the model focus on relevant image regions. A larger, more diverse dataset provides the model with more examples of complex reasoning scenarios. Increasing the image embedding size may help but is not as targeted. Decreasing the learning rate is a general optimization technique, and using a simpler RNN would likely degrade performance.
NEW QUESTION # 34
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