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Guide for AI Infrastructure and Operations

Quiz AI Infrastructure and Operations

Free Test
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Quiz

1/10
An enterprise is deploying a large-scale AI model for real-time image recognition. They face challenges
with scalability and need to ensure high availability while minimizing latency. Which combination of
NVIDIA technologies would best address these needs?
Select the answer
1 correct answer
A.
NVIDIA CUDA and NCCL
B.
NVIDIA Triton Inference Server and GPUDirect RDMA
C.
NVIDIA DeepStream and NGC Container Registry
D.
NVIDIA TensorRT and NVLink

Quiz

2/10
A company is using a multi-GPU server for training a deep learning model. The training process is
extremely slow, and after investigation, it is found that the GPUs are not being utilized efficiently. The
system uses NVLink, and the software stack includes CUDA, cuDNN, and NCCL. Which of the following
actions is most likely to improve GPU utilization and overall training performance?
Select the answer
1 correct answer
A.
Increase the batch size
B.
Update the CUDA version to the latest release
C.
Disable NVLink and use PCIe for inter-GPU communication
D.
Optimize the model's code to use mixed-precision training

Quiz

3/10
In an AI data center, you are responsible for monitoring the performance of a GPU cluster used for large-
scale model training. Which of the following monitoring strategies would best help you identify and
address performance bottlenecks?
Select the answer
1 correct answer
A.
Monitor only the GPU utilization metrics to ensure that all GPUs are being used at full capacity.
B.
Focus on job completion times to ensure that the most critical jobs are being finished on schedule.
C.
Track CPU, GPU, and network utilization simultaneously to identify any resource imbalances that
could lead to bottlenecks.
D.
Use predictive analytics to forecast future GPU utilization, adjusting resources before bottlenecks
occur.

Quiz

4/10
You are assisting a senior data scientist in analyzing a large dataset of customer transactions to identify
potential fraud. The dataset contains several hundred features, but the senior team member advises you
to focus on feature selection before applying any machine learning models. Which approach should you
take under their supervision to ensure that only the most relevant features are used?
Select the answer
1 correct answer
A.
Select features randomly to reduce the number of features while maintaining diversity.
B.
Ignore the feature selection step and use all features in the initial model.
C.
Use correlation analysis to identify and remove features that are highly correlated with each other.
D.
Use Principal Component Analysis (PCA) to reduce the dataset to a single feature.

Quiz

5/10
You are evaluating the performance of two AI models on a classification task. Model A has an accuracy
of 85%, while Model B has an accuracy of 88%. However, Model A's F1 score is 0.90, and Model B's F1
score is 0.88. Which model would you choose based on the F1 score, and why?
Select the answer
1 correct answer
A.
Model A - The F1 score is higher, indicating better balance between precision and recall.
B.
Model B - The higher accuracy indicates overall better performance.
C.
Neither - The choice depends entirely on the specific use case.
D.
Model B - The F1 score is lower but accuracy is more reliable.

Quiz

6/10
Which NVIDIA hardware and software combination is best suited for training large-scale deep learning
models in a data center environment?
Select the answer
1 correct answer
A.
NVIDIA Jetson Nano with TensorRT for training.
B.
NVIDIA DGX Station with CUDA toolkit for model deployment.
C.
NVIDIA A100 Tensor Core GPUs with PyTorch and CUDA for model training.
D.
NVIDIA Quadro GPUs with RAPIDS for real-time analytics.

Quiz

7/10
A healthcare company is looking to adopt AI for early diagnosis of diseases through medical imaging.
They need to understand why AI has become so effective recently. Which factor should they consider as
most impactful in enabling AI to perform complex tasks like image recognition at scale?
Select the answer
1 correct answer
A.
Advances in GPU technology, enabling faster processing of large datasets required for AI tasks.
B.
Development of new programming languages specifically for AI.
C.
Increased availability of medical imaging data, allowing for better machine learning model training.
D.
Reduction in data storage costs, allowing for more data to be collected and stored.

Quiz

8/10
Which of the following networking features is MOST critical when designing an AI environment to handle
large-scale deep learning model training?
Select the answer
1 correct answer
A.
Enabling network redundancy to prevent single points of failure.
B.
Implementing network segmentation to isolate different parts of the AI environment.
C.
High network throughput with low latency between compute nodes.
D.
Using Wi-Fi for flexibility in connecting compute nodes.

Quiz

9/10
Your AI data center is running multiple high-performance GPU workloads, and you notice that certain
servers are being underutilized while others are consistently at full capacity, leading to inefficiencies.
Which of the following strategies would be most effective in balancing the workload across your AI data
center?
Select the answer
1 correct answer
A.
Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling
B.
Use Horizontal Scaling to Add More Servers
C.
Manually Reassign Workloads Based on Current Utilization
D.
Increase Cooling Capacity in the Data Center

Quiz

10/10
You are tasked with deploying a machine learning model into a production environment for real-time
fraud detection in financial transactions. The model needs to continuously learn from new data and adapt
to emerging patterns of fraudulent behavior. Which of the following approaches should you implement to
ensure the model's accuracy and relevance over time?
Select the answer
1 correct answer
A.
Continuously retrain the model using a streaming data pipeline
B.
Run the model in parallel with rule-based systems to ensure redundancy
C.
Deploy the model once and retrain it only when accuracy drops significantly
D.
Use a static dataset to retrain the model periodically
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Info quiz:

  • Quiz name:AI Infrastructure and Operations
  • Total number of questions:300
  • Number of questions for the test:50
  • Pass score:80%

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