MLA-C01 RELIABLE DUMPS SHEET, VALID MLA-C01 EXAM PREP

MLA-C01 Reliable Dumps Sheet, Valid MLA-C01 Exam Prep

MLA-C01 Reliable Dumps Sheet, Valid MLA-C01 Exam Prep

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q24-Q29):

NEW QUESTION # 24
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

  • A. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
  • B. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • C. Create a model group for each category. Move the existing models into these category model groups.
  • D. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

Answer: D

Explanation:
Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.


NEW QUESTION # 25
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

  • A. Use SageMaker script mode and premade images for ML frameworks.
  • B. Use SageMaker built-in algorithms to train the proprietary datasets.
  • C. Purchase similar production models through AWS Marketplace.
  • D. Build a container on AWS that includes custom packages and a choice of ML frameworks.

Answer: A

Explanation:
SageMaker script mode allows you to bring existing custom Python scripts and run them on AWS with minimal changes. SageMaker provides prebuilt containers for ML frameworks like PyTorch, simplifying the migration process. This approach enables the company to leverage their existing Python scripts and domain knowledge while benefiting from the scalability and managed environment of SageMaker. It requires the least effort compared to building custom containers or retraining models from scratch.


NEW QUESTION # 26
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

  • A. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  • B. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
  • C. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  • D. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.

Answer: A

Explanation:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.


NEW QUESTION # 27
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Answer:

Explanation:

Explanation:
* City (name):One-hot encoding
* Type_year (type of home and year the home was built):Feature splitting
* Size of the building (square feet or square meters):Standardized distribution
* City (name): One-hot encoding
* Why?The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for eachunique category (e.g., cities like "New York" or "Los Angeles"), which the model can interpret.
* Type_year (type of home and year the home was built): Feature splitting
* Why?"Type_year" combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: "Type of home" and
"Year built," enabling the model to process each feature independently.
* Size of the building (square feet or square meters): Standardized distribution
* Why?Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.


NEW QUESTION # 28
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

  • A. Apply statistics from a well-known dataset to normalize the production samples.
  • B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
  • C. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
  • D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

Answer: B

Explanation:
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.


NEW QUESTION # 29
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