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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q43-Q48):
NEW QUESTION # 43
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?
- A. Both Phase 1 and Phase 2 are examples of inferencing.
- B. Phase 1 is inferencing, and Phase 2 is training.
- C. Phase 1 is training, and Phase 2 is inferencing.
- D. Both Phase 1 and Phase 2 are examples of training.
Answer: C
NEW QUESTION # 44
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)
- A. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
- B. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
- C. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
- D. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
- E. Use a RAG architecture for the sales forecasting model.
- F. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
Answer: A,B,D
NEW QUESTION # 45
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?
- A. The storage system (ASA) is unable to provide sufficient read throughput.
- B. The server's network interface is saturated.
- C. The server's CPU is overloaded.
- D. The application is experiencing excessive memory paging.
Answer: B
NEW QUESTION # 46
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)
- A. Object-based storage
- B. POSIX-based file systems
- C. File-based systems
- D. Parallel file systems
Answer: A,D
NEW QUESTION # 47
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?
- A. It sends an email notification to the administrator to manually reallocate the GPUs.
- B. It automatically terminates all low-priority jobs to free up the entire cluster.
- C. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
- D. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
Answer: D
NEW QUESTION # 48
......
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