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NEW QUESTION # 222
A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.
Which solution will optimize the scaling process without affecting response times?
Answer: D
Explanation:
Amazon SageMaker auto scaling uses cooldown periods to control how frequently scaling activities occur.
When scale-out happens too quickly, new instances may receive traffic before they are fully initialized, leading to inefficiencies and latency.
AWS documentation recommends increasing the scale-out cooldown period to give newly launched instances sufficient time to initialize and become healthy before additional scaling events occur. This ensures stable performance during traffic spikes without impacting response times.
Multi-model endpoints address model hosting efficiency, not scaling timing. API Gateway and Lambda add unnecessary latency and complexity. Decreasing scale-in cooldown does not address scale-out issues.
Therefore, Option D is the correct and AWS-aligned solution.
NEW QUESTION # 223
A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents.
Which solution will meet this requirement with the LEAST operational effort?
Answer: D
Explanation:
Option A is correct because Amazon Rekognition provides a built-in EyeDirection attribute that indicates the direction a person's eyes are gazing, expressed through pitch and yaw. AWS documentation describes EyeDirection as showing where the eyes are looking independently of head pose. For a driver-monitoring use case, this is directly useful for identifying whether a driver is looking away from the road and may be distracted.
AWS also announced that Rekognition's eye gaze direction detection is intended to support safety use cases and to help identify where users focus their attention. That makes it a strong match for monitoring in-vehicle cameras for distraction detection. Because Rekognition is a fully managed service with ready-to-use computer vision capabilities, it requires far less operational effort than collecting data, labeling it, training a custom model in SageMaker, and maintaining that model over time.
The other options are less appropriate. Option B could work technically, but building and maintaining a custom SageMaker model would require significantly more engineering and operational work than using a managed Rekognition feature. Option C introduces unnecessary complexity by adding a third-party system when AWS already provides a directly relevant managed capability. Option D is unrelated because Amazon Comprehend analyzes text, while the input here is camera footage. Since the question asks for the solution with the least operational effort , the best AWS-aligned answer is to use the managed Rekognition eye gaze detection capability. Therefore, the fully verified answer is A .
NEW QUESTION # 224
A company is building a conversational AI assistant on Amazon Bedrock. The company is using Retrieval Augmented Generation (RAG) to reference the company's internal knowledge base. The AI assistant uses the Anthropic Claude 4 foundation model (FM).
The company needs a solution that uses a vector embedding model, a vector store, and a vector search algorithm.
Which solution will develop the AI assistant with the LEAST development effort?
Answer: A
Explanation:
Amazon Kendra Experience Builder provides a fully managed, low-code solution for building conversational search and question-answering applications. AWS documentation states that Kendra natively supports semantic search, vector embeddings, and vector-based retrieval, making it well suited for RAG-style applications with minimal development effort.
When integrated with Amazon Bedrock, Kendra can act as the retrieval layer, handling document ingestion, indexing, embedding generation, and relevance ranking automatically. This eliminates the need to manually manage embedding models, vector databases, and search logic.
Options B and C require custom schema design, vector indexing, query logic, and operational management of PostgreSQL instances. Although pgvector supports vector search, it significantly increases development and maintenance effort. Option D is unrelated to vector search and is used only for metadata cataloging.
AWS explicitly positions Amazon Kendra as the fastest way to build enterprise-grade conversational assistants that integrate with foundation models.
Therefore, Option A is the correct and most AWS-aligned solution.
NEW QUESTION # 225
A company uses a training job on Amazon SageMaker Al to train a neural network. The job first trains a model and then evaluates the model's performance ag test dataset. The company uses the results from the evaluation phase to decide if the trained model will go to production.
The training phase takes too long. The company needs solutions that can shorten training time without decreasing the model's final performance.
Select the correct solutions from the following list to meet the requirements for each description. Select each solution one time or not at all. (Select THREE.)
. Change the epoch count.
. Choose an Amazon EC2 Spot Fleet.
Change the batch size.
. Use early stopping on the training job.
Use the SageMaker Al distributed data parallelism (SMDDP) library.
. Stop the training job.
Answer:
Explanation:
Explanation:
Change the number of samples used in each iteration of training
Correct selection:
Change the batch size
Why:
Increasing the batch size reduces the number of iterations per epoch, which can significantly shorten training time while maintaining model quality when tuned appropriately. AWS explicitly recommends batch size tuning as a primary performance optimization.
Increase the number of instances used during training
Correct selection:
Use the SageMaker AI distributed data parallelism (SMDDP) library
Why:
SMDDP is designed to efficiently distribute training data across multiple GPU instances with optimized gradient synchronization. This accelerates training without affecting model convergence or accuracy, unlike naive scaling approaches.
Stop training before the maximum number of epochs are reached if performance is sufficient and not improving Correct selection:
Use early stopping on the training job
Why:
Early stopping automatically terminates training when validation metrics stop improving. AWS recommends this to reduce wasted compute time while preserving optimal model performance.
NEW QUESTION # 226
An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
* Random Cut Forest (RCF) algorithm
* Semantic segmentation algorithm
* Sequence-to-Sequence (seq2seq) algorithm
Answer:
Explanation:
Explanation:
Use case 1:
Summarize the text of a research paper
## Sequence-to-Sequence (seq2seq) algorithm
Why:
Seq2seq models are designed for natural language generation tasks such as text summarization, translation, and paraphrasing. AWS documentation explicitly lists text summarization as a primary use case for the SageMaker seq2seq algorithm.
Use case 2:
Scan every pixel of an image to help self-driving cars identify objects in their path
## Semantic segmentation algorithm
Why:
Semantic segmentation performs pixel-level classification, assigning a class label to every pixel in an image.
This is exactly what is required for applications such as autonomous driving, road scene understanding, and object boundary detection.
Use case 3:
Identify abnormal data points in a dataset
## Random Cut Forest (RCF) algorithm
Why:
Random Cut Forest is an unsupervised anomaly detection algorithm. AWS SageMaker RCF is purpose-built to identify outliers, unusual patterns, and anomalies in numerical datasets, making it ideal for fraud detection, monitoring, and abnormal data point detection.
NEW QUESTION # 227
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