2025 AUTHORITATIVE RELIABLE AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TEST ONLINE | AWS CERTIFIED MACHINE LEARNING - SPECIALTY 100% FREE STUDY CENTER

2025 Authoritative Reliable AWS-Certified-Machine-Learning-Specialty Test Online | AWS Certified Machine Learning - Specialty 100% Free Study Center

2025 Authoritative Reliable AWS-Certified-Machine-Learning-Specialty Test Online | AWS Certified Machine Learning - Specialty 100% Free Study Center

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q268-Q273):

NEW QUESTION # 268
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

  • A. The dataset cannot be accurately represented using the regression model
  • B. There are too many variables in the model
  • C. The model might have prediction errors over a range of target values.
  • D. The model is predicting its target values perfectly.

Answer: D


NEW QUESTION # 269
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

  • A. Build the Docker container to be NVIDIA-Docker compatible.
  • B. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body
  • C. Organize the Docker container's file structure to execute on GPU instances.
  • D. Bundle the NVIDIA drivers with the Docker image.

Answer: A


NEW QUESTION # 270
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

  • A. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
  • B. Drop the features with low correlation scores by using a Jupyter notebook.
  • C. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
  • D. Concatenate the features with high correlation scores by using a Jupyter notebook.

Answer: C

Explanation:
The best feature engineering strategy for this scenario is to apply dimensionality reduction by using the principal component analysis (PCA) algorithm. PCA is a technique that transforms a large set of correlated features into a smaller set of uncorrelated features called principal components. This can help reduce the complexity and noise in the data, improve the performance and interpretability of the model, and avoid overfitting. Amazon SageMaker provides a built-in PCA algorithm that can be used to perform dimensionality reduction on tabular data. The ML specialist can use Amazon SageMaker to train and deploy the PCA model, and then use the output of the PCA model as the input for the classification model.
Dimensionality Reduction with Amazon SageMaker
Amazon SageMaker PCA Algorithm


NEW QUESTION # 271
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

  • A. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.
  • B. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.
  • C. Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects
  • D. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
  • E. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
  • F. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.

Answer: B,C,E

Explanation:
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
* B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
* C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
* E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
* A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
* D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:
GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
* F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
1: InvokeEndpoint - Amazon SageMaker
2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
4: AmazonAthenaFullAccess - AWS Identity and Access Management
5: GetRecord - Amazon SageMaker Feature Store Runtime
[Invoke a Multi-Model Endpoint - Amazon SageMaker]


NEW QUESTION # 272
This graph shows the training and validation loss against the epochs for a neural network.
The network being trained is as follows:
* Two dense layers, one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

  • A. Increasing the number of epochs
  • B. Random initialization of weights with appropriate seed
  • C. Early stopping
  • D. Adding another layer with the 100 neurons

Answer: A


NEW QUESTION # 273
......

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