MLS-C01 VALID EXAM PAPERS & MLS-C01 EXAMS

MLS-C01 Valid Exam Papers & MLS-C01 Exams

MLS-C01 Valid Exam Papers & MLS-C01 Exams

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q299-Q304):

NEW QUESTION # 299
An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.
Which data transformation step should the data scientist take to improve the predictions of the model?

  • A. Quantile binning
  • B. One-hot encoding
  • C. Normalization
  • D. Cartesian product transformation

Answer: A

Explanation:
Quantile binning is a data transformation technique that can be used to handle skewed and non-linear numerical features. It divides the range of a feature into equal-sized bins based on the percentiles of the data.
Each bin is assigned a numerical value that represents the midpoint of the bin. This way, the feature values are transformed into a more uniform distribution that can improve the performance of linear models. Quantile binning can also reduce the impact of outliers and noise in the data.
One-hot encoding, Cartesian product transformation, and normalization are not suitable for this scenario. One- hot encoding is used to transform categorical features into binary features. Cartesian product transformation is used to create new features by combining existing features. Normalization is used to scale numerical features to a standard range, but it does not change the shape of the distribution. References:
* Data Transformations for Machine Learning
* Quantile Binning Transformation


NEW QUESTION # 300
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

  • A. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
  • B. Train a custom ARIMA model to forecast demand for the new product.
  • C. Train a custom XGBoost model to forecast demand for the new product
  • D. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Answer: D

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.


NEW QUESTION # 301
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create one-hot word encoding vectors.
  • B. Download word embeddings pre-trained on a large corpus.
  • C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  • D. Create word embedding vectors that store edit distance with every other word.

Answer: B

Explanation:
As it is a interactive online dictionary, we need pre-trained word embedding thus the answer is D.
In addition, there is no mention that the online dictonary is unique and does not have a pre- trained word embedding.


NEW QUESTION # 302
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. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
  • B. Bundle the NVIDIA drivers with the Docker image.
  • C. Build the Docker container to be NVIDIA-Docker compatible.
  • D. Organize the Docker container's file structure to execute on GPU instances.

Answer: C

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible.
Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image.
For more information about nvidia-docker, see NVIDIA/nvidia-docker.


NEW QUESTION # 303
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

  • A. Naive Bayesian classifier
  • B. Single Perceptron with sigmoidal activation function
  • C. Decision tree
  • D. Linear support vector machine (SVM)

Answer: A


NEW QUESTION # 304
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