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Questions gratuites MLS-C01 — AWS Certified Machine Learning - Specialty

Téléchargez gratuitement 60 questions d'entraînement pour la certification MLS-C01 proposée par AWS. Toutes les questions sont accompagnées de corrections détaillées avec explications techniques.

Caractéristiques de l'examen blanc

Code de certificationMLS-C01
ÉditeurAWS
Nombre de questions60
TypeQCM avec 4 réponses possibles
Niveauassociate
CatégorieIT
Prix100% gratuit

Aperçu de 8 questions représentatives

Voici un échantillon aléatoire de 8 questions tirées de notre base d'entraînement MLS-C01. Pour accéder aux 60 questions complètes, lancez l'examen blanc gratuitement.

Question 1
You need to assess whether your dataset is suitable for ML or requires more data. Which indicators suggest insufficient data? (Choose THREE)
  1. **Type** : multiple_choice
  2. High variance in cross-validation results
  3. Large gap between training and validation performance
  4. Very few examples per class (<100)
  5. Perfect training accuracy with large dataset
  6. Consistent cross-validation performance
Question 2
You need to implement A/B testing comparing two model versions in production. Which SageMaker capability enables A/B testing?
  1. Multi-variant endpoints with traffic distribution
  2. Deploy only one model version
  3. Manual switching between models
  4. No A/B testing support
  5. , separate CloudWatch metrics per variant enabling performance comparison, automatic load balancing, and dynamic traffic shifting without endpoint recreation. Variants share endpoint reducing infrastructure costs compared to separate endpoints. Deploy only one model prevents comparison, manual switching lacks traffic splitting and metrics separation, and SageMaker explicitly supports production A/B testing. Multi-variant pattern: deploy baseline model (variant A) + new model (variant B) → split traffic (e.g., 95%/5%) → compare metrics (latency, accuracy via Model Monitor) → gradually shift traffic to winner or rollback = data-driven model deployment decisions. Essential for validating model improvements in production with real traffic before full rollout.
Question 3
Your model training requires distributed training across multiple GPUs to reduce training time. Which SageMaker feature enables distributed training?
  1. SageMaker distributed training with data parallelism or model parallelism
  2. Single GPU training only
  3. Manual cluster setup
  4. Sequential single-node training
Question 4
Your model pipeline requires automated retraining on new data. Which components enable automated retraining? (Choose THREE)
  1. **Type** : multiple_choice
  2. EventBridge schedule triggering pipeline
  3. S3 event triggering on new data
  4. SageMaker Pipelines with parameters
  5. Manual retraining only
  6. Never retrain models
Question 5
Your recommendation system requires learning from implicit feedback (clicks, views) rather than explicit ratings. Which algorithm handles implicit feedback?
  1. SageMaker Factorization Machines optimized for sparse data
  2. Linear regression
  3. Image classification
  4. Random selection
Question 6
Your training data has privacy requirements restricting how it can be accessed. Which techniques enable privacy-preserving ML? (Choose TWO)
  1. Differential privacy adding noise to preserve privacy
  2. Federated learning training without centralizing data
  3. Share all raw data publicly
  4. No privacy protections
  5. Ignore privacy regulations
Question 7
Your model inference requires data preprocessing consistency between training and inference. Which pattern ensures preprocessing consistency?
  1. SageMaker inference pipelines or containerized preprocessing
  2. Duplicate preprocessing code in different languages
  3. No consistency mechanism
  4. Hope preprocessing matches
Question 8
Your ML project requires reproducibility of training runs. Which practices ensure reproducible training?
  1. Set random seeds, version data/code, track experiments, use SageMaker Pipelines
  2. Random initialization every time
  3. Undocumented code changes
  4. No experiment tracking
  5. , versioned code (Git SHA), logged hyperparameters, recorded training environment (Docker image), experiment tracking (SageMaker Experiments), automated pipelines (SageMaker Pipelines) = scientifically valid ML engineering. Critical for debugging, compliance, and knowledge sharing.

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