SAS Natural Language and Computer Vision (A00-408) Certification Exam Sample Questions

Get A00-408 Dumps Free, SAS Natural Language and Computer Vision PDF and Dumps, and A00-408 Free Download for comprehensive exam preparation.Welcome! Preparing for the SAS Viya Natural Language Processing and Computer Vision (A00-408) certification exam can be a daunting task, but we're here to make it easier for you. Here are the sample questions that will help you become familiar with the SAS A00-408 exam style and structure. We encourage you to try our Demo SAS Natural Language and Computer Vision Certification Practice Exam to measure your understanding of the exam structure in an environment that simulates the actual test environment.

Why Use Our SAS Natural Language and Computer Vision Sample Questions?

To make your preparation easier for the SAS A00-408 exam, we strongly recommend you to use our Premium SAS Natural Language and Computer Vision Certification Practice Exam. According to our survey with certified candidates, you can easily score more than 85% in your actual SAS Certified Specialist - Natural Language Processing and Computer Vision Using SAS Viya exam if you score 100% in our premium certification practice exams.

SAS A00-408 Sample Questions:

01. Noor is reviewing how a SAS Deep Learning CNN scans shelf-camera images to flag empty facings. A colleague explains that the convolutional layers rely on a set of small, learned matrices that slide across the image.
What are these learned matrices commonly called, and what do they do?
a) Activation thresholds — each one slides across the image and decides whether a local region contains the pattern it was trained to detect.
b) Pooling windows — each one averages or takes the maximum of a region to shrink the feature map.
c) Fully-connected weights — each one links every pixel in the image directly to a single class score.
d) Filters (kernels) — each one learns to respond strongly to a specific local visual pattern, such as an edge, color transition, or texture.
 
02. Yuki is comparing two candidate CNN architectures for a satellite land-cover classifier: Architecture X has fewer convolutional layers and fewer filters per layer; Architecture Y has roughly triple the layers and filters. The labeled satellite-tile training set is modest in size and unlikely to grow soon.
Which consideration should most directly guide Yuki's choice between the two architectures?
a) Architecture Y's larger capacity raises the risk of overfitting on a modest, static training set, so Yuki should weigh that risk against Architecture X's more limited capacity rather than assuming bigger is automatically better.
b) Pooling layers already remove the overfitting risk that comes from adding more convolutional filters.
c) Architecture Y's extra layers and filters mainly reduce the number of labeled images required, making the modest, static training-set size less of a concern than it would be for the smaller Architecture X, which needs even less data to train well.
d) Architecture X's smaller size is the deciding factor, since scoring speed matters more for this task than accuracy on held-out tiles.
 
03. Ingrid is deciding between a shallow CNN with fewer filters per layer and a deeper CNN with more filters per layer for her camera-trap species classifier.
In general terms, what does adding more layers and more filters give the network?
a) A shorter list of hyperparameters to configure, since deeper networks with more filters need fewer architecture choices than shallow ones with only a handful of layers.
b) Greater representational capacity — the ability to learn more complex patterns — though this capacity must be matched to the amount of available labeled training data.
c) A smaller number of trainable parameters than a shallower network with fewer filters per layer.
d) The ability to skip the pooling stage entirely, since deeper networks no longer need down-sampling.
 
04. An editorial team classifies incoming news articles into categories such as Sports for routing to the correct desk. The Sports category's Boolean rule set references an underlying concept definition that identifies sports-related terminology.
Which statement best describes this relationship between concepts and categories?
a) Concept definitions can only be used for entity extraction; category rules are built directly from a term-document matrix instead of referencing any concept definition
b) A concept is simply a renamed category with no functional difference between the two
c) A category is often built by layering additional Boolean/rule logic on top of one or more concept definitions to classify documents into an analyst-defined group
d) Categories are generated straight from topic-model output, and cannot reference a concept definition once they are built
 
05. Dana's SAS Deep Learning land-cover model applies an activation function immediately after each convolutional layer's filters are applied.
Why is this activation step needed in the CNN architecture?
a) It introduces non-linearity into the network, letting the stacked layers represent more complex relationships than a purely linear chain of convolutions could.
b) It reassigns the learned filter weights so the next training pass starts from a fresh random state, giving the network a chance to discover better filters than the ones it started training with initially.
c) It converts the image from color to grayscale ahead of the first convolutional layer.
d) It reduces the spatial dimensions of the feature maps to cut down on computation.
 
06. Sofia is reviewing how filters behave at different depths in her document-image CNN.
Which statement correctly describes how the learned filters typically differ between the network's earliest convolutional layers and its deeper convolutional layers?
a) Filters at every depth detect the same class of pattern; only the number of filters changes with depth.
b) Filter behavior is fixed at model definition and is not affected by the training data used.
c) Deeper-layer filters operate on the raw pixel image directly, bypassing the feature maps produced by earlier layers entirely, the same way the very first convolutional layer in the network does.
d) Early-layer filters tend to detect simple, low-level patterns such as edges and textures, while deeper-layer filters combine those into more complex, higher-level shapes and structures.
 
07. Liam's camera-trap species-detection model inserts a pooling layer after several of the early convolutional layers, well before the network reaches its fully-connected layer.
Beyond adding some robustness to small shifts in the animal's position, what is the main practical benefit of doing this?
a) It converts the grayscale camera-trap images into color images for richer feature detection.
b) It relabels the training images with corrected species tags before the next training epoch.
c) It shrinks the spatial size of the feature maps, which reduces the number of computations and parameters needed in the layers that follow.
d) It increases the number of learned filters available to the next convolutional layer.
 
08. Wei's CNN has passed a panel image through several convolutional and pooling layers, producing a rich set of down-sampled feature maps.
What happens at the fully-connected (output) layer of this image-classification architecture?
a) The feature maps are down-sampled further to reduce their spatial dimensions.
b) The extracted features are combined and weighted to produce a single class decision for the whole image, such as defect versus no-defect.
c) New filters are learned at this stage to detect additional edge patterns not found by any of the earlier convolutional layers in the network's earlier stages.
d) The raw image is re-loaded from the caslib to refresh the input before scoring.
 
09. Before training an image classification model, a quality-control team loads a set of product-defect photographs into CAS and wants to explore the image set first.
Which activity best reflects appropriate exploration of the image set at this stage?
a) Reviewing the images for consistency in dimensions, format, and label coverage across the set.
b) Running the scoring action to generate predictions before any images have been loaded.
c) Applying a stop list to remove low-information terms from the image file names.
d) Immediately defining the CNN's convolutional and pooling layer counts before reviewing any images.
 
10. Rosa is training a shelf-gap CNN with a modest set of labeled shelf images and wants to sanity-check her understanding of capacity and overfitting before scaling up the architecture.
Which two statements about CNN capacity and overfitting are accurate?
(Choose two.)
a) Adding more convolutional layers and filters increases the network's representational capacity, which can help it fit more complex patterns.
b) Pooling layers remove the overfitting risk introduced by adding more convolutional filters.
c) A network with high capacity trained on a small, limited image set is more prone to fitting noise in the training images rather than generalizing to new ones.
d) Overfitting risk decreases as network depth increases, because deeper networks are inherently better at generalizing.

Answers:

Question: 1 Answer: d Question: 2 Answer: a
Question: 3 Answer: b Question: 4 Answer: c
Question: 5 Answer: a Question: 6 Answer: d
Question: 7 Answer: c Question: 8 Answer: b
Question: 9 Answer: a Question: 10 Answer: a, c

Note: Please write to us at feedback@analyticsexam.com if you find any data entry errors in these SAS Viya Natural Language Processing and Computer Vision (A00-408) sample questions.

Get Started Today!

Equip yourself with the best resources and practice exams to ace your SAS Certified Specialist - Natural Language Processing and Computer Vision Using SAS Viya exam. Explore our comprehensive study materials and take the first step towards certification success.

Rating: 4.8 / 5 (112 votes)