A social media company processes user posts in real-time to train a sentiment analysis model. The model must be updated every few minutes with new posts. The company also runs a daily batch job that retrains the model on the full historical dataset. The company wants to use a single pipeline that handles both real-time and batch data. Which design is most appropriate?

Use a lambda architecture with separate real-time and batch layers

Use a single pipeline that processes data in a single large batch daily

Use only a batch pipeline that runs every few minutes

Use only a real-time streaming pipeline

A financial firm collects transaction data continuously and needs to train a fraud detection model. The model must be updated with new data every hour, but the training job can tolerate a few minutes of delay. The firm wants to minimize the complexity of the pipeline. Which ingestion approach should they choose?

Real-time streaming ingestion with a message queue

Continuous sync that copies new files every minute

Batch ingestion that runs every hour

Manual ingestion triggered by an operator

A research lab runs a distributed training job on a GPU cluster. The training nodes need to read a shared dataset concurrently, and the dataset is too large to copy to each node's local disk. The lab wants the lowest-latency access for the training nodes while keeping a single consistent copy of the data. Which data access method should they use?

Use SMB to mount the dataset from a Windows file server

Copy the dataset to each node's local NVMe drive before training

Access the dataset via S3 object storage using HTTP GETs

Mount the dataset via NFS from a shared storage appliance

A research institute runs a large-scale distributed training job across multiple GPU nodes in a single data center. The training data is stored on a central NAS. The training is network-bound, and the institute wants to reduce the network traffic between the NAS and the compute nodes. Which strategy is most effective?

Move the training data to a different data center

Cache a subset of the data on each node's local SSD

Increase the number of NAS heads to serve more requests

Use a faster network protocol like RDMA

NetAppCertified AI Expert

Domain 5Objective 3

Describe How to Access and Move Data for AI Workloads CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Part of the AI Common Challenges domain, which accounts for 22% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–13 in this domain), expect 1–2 from this objective — we provide 33 practice questions to prepare you well beyond it. (estimate)

33questions here

7free pages

7concepts

22%of the exam

Questions 16–20

16foundation · easy
Which data ingestion pattern is best suited for processing large volumes of historical data in scheduled intervals, such as nightly training dataset updates?
Select an answer first
17expert · hard
A social media company processes user posts in real-time to train a sentiment analysis model. The model must be updated every few minutes with new posts. The company also runs a daily batch job that retrains the model on the full historical dataset. The company wants to use a single pipeline that handles both real-time and batch data. Which design is most appropriate?
Select an answer first
18application · medium
A financial firm collects transaction data continuously and needs to train a fraud detection model. The model must be updated with new data every hour, but the training job can tolerate a few minutes of delay. The firm wants to minimize the complexity of the pipeline. Which ingestion approach should they choose?
Select an answer first
19application · medium
A research lab runs a distributed training job on a GPU cluster. The training nodes need to read a shared dataset concurrently, and the dataset is too large to copy to each node's local disk. The lab wants the lowest-latency access for the training nodes while keeping a single consistent copy of the data. Which data access method should they use?
Select an answer first
20application · medium
A research institute runs a large-scale distributed training job across multiple GPU nodes in a single data center. The training data is stored on a central NAS. The training is network-bound, and the institute wants to reduce the network traffic between the NAS and the compute nodes. Which strategy is most effective?
Select an answer first

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Describe How to Access and Move Data for AI Workloads CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Questions 16–20

Which data ingestion pattern is best suited for processing large volumes of historical data in scheduled intervals, such as nightly training dataset updates?

Keep studying — AI Common Challenges