AWS MLA-C01 Certification Exam Sample Questions

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AWS MLA-C01 Sample Questions:

01. What scaling policy would you use for a model endpoint with fluctuating but predictable daily traffic patterns?
a) On-demand scaling
b) Time-based scaling
c) Manual scaling
d) Fixed scaling
 
02. Which of the following is a built-in algorithm in Amazon SageMaker suitable for clustering problems?
a) K-Means
b) XGBoost
c) DeepAR
d) Linear Learner
 
03. A model deployed to production shows significant performance degradation over time. You suspect that the input data distribution has shifted compared to the training data.
What actions should you take to diagnose and address the issue?
(Choose three)
a) Change the model algorithm to one more robust to data drift.
b) Retrain the model using updated data.
c) Reduce the batch size for inference.
d) Evaluate the performance metrics for the updated test dataset.
e) Use SageMaker Model Monitor to detect data drift.
 
04. Which methods can reduce the training time of a deep learning model?
​(Choose two)
a) Reducing the number of epochs
b) Increasing the batch size
c) Implementing distributed training
d) Using dropout during training
 
05. What are the steps to integrate SageMaker Pipelines into an AWS CodePipeline workflow?
1. Trigger the pipeline using source code changes in CodeCommit.
2. Monitor pipeline execution and validate results.
3. Integrate the SageMaker pipeline as a step in CodePipeline.
4. Define the pipeline structure in SageMaker Pipelines.
a) 1 → 2 → 3 → 4
b) 2 → 4 → 1 → 3
c) 3 → 1 → 4 → 2
d) 4 → 3 → 2 → 1
 
06. Which type of machine learning problem would you solve using a regression model?
a) Predicting stock prices for the next week
b) Identifying spam emails
c) Clustering customer profiles
d) Detecting fraudulent transactions
 
07. What are common metrics used for evaluating classification models?
​(Choose two)
a) Mean Squared Error (MSE)
b) RMSE
c) Recall
d) Precision
 
08. Which feature engineering technique involves transforming skewed numerical data to approximate a normal distribution?
a) Log transformation
b) One-hot encoding
c) Standardization
d) Feature binning
 
09. Your team is evaluating a binary classification model trained for fraud detection. The model achieves 95% accuracy on the test set, but customer complaints suggest it frequently misses fraudulent transactions.
What actions should you take to resolve this issue?
(Choose three)
a) Use precision and recall to evaluate the model instead of accuracy.
b) Adjust the decision threshold to improve recall.
c) Add more features to the training dataset.
d) Switch to a regression model for better fraud detection.
e) Perform a detailed analysis of false negatives.
 
10. What is the best storage solution for temporary storage of large intermediate datasets generated during a machine learning process?
a) Amazon S3
b) Amazon EBS
c) Amazon Glacier
d) Amazon DynamoDB

Answers:

Question: 01
Answer: b		 Question: 02
Answer: a		 Question: 03
Answer: b, d, e		 Question: 04
Answer: b, c		 Question: 05
Answer: d
Question: 06
Answer: a		 Question: 07
Answer: c, d		 Question: 08
Answer: a		 Question: 09
Answer: a, b, e		 Question: 10
Answer: b

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