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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q41-Q46):NEW QUESTION # 41
A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:
The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

• A. Create three columns to encode the color in RGB format.
• B. Add new columns that store one-hot representation of colors.
• C. Apply integer transformation and set Red = 1, White = 5, and Green = 10.
• D. Replace the color name string by its length.
• E. Replace each color name by its training set frequency.
  

Answer: A,B
Explanation:
In this scenario, the specialist should use one-hot encoding and RGB encoding to allow the regression model to learn from the Wall_Color data. One-hot encoding is a technique used to convert categorical data into numerical data. It creates new columns that store one-hot representation of colors. For example, a variable named color has three categories: red, green, and blue. After one-hot encoding, the new variables should be like this:
One-hot encoding can capture the presence or absence of a color, but it cannot capture the intensity or hue of a color. RGB encoding is a technique used to represent colors in a digital image. It creates three columns to encode the color in RGB format. For example, a variable named color has three categories: red, green, and blue. After RGB encoding, the new variables should be like this:
RGB encoding can capture the intensity and hue of a color, but it may also introduce correlation among the three columns. Therefore, using both one-hot encoding and RGB encoding can provide more information to the regression model than using either one alone.
References:
* Feature Engineering for Categorical Data
* How to Perform Feature Selection with Categorical Data

NEW QUESTION # 42
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

• A. Amazon Comprehend syntax analysts and entity detection
• B. Amazon SageMaker BlazingText allow mode
• C. Natural Language Toolkit (NLTK) stemming and stop word removal
• D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
  

Answer: A

NEW QUESTION # 43
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

• A. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
• B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
• C. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
• D. The learning rate should be increased because the optimization process was trapped at a local minimum.
  

Answer: D

NEW QUESTION # 44
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

• A. Create a NAT gafjway within the corporate VPC.
• B. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
• C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
• D. Route Amazon SageMaker traffic through an on-premises network.
  

Answer: C

NEW QUESTION # 45
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?

• A. Use AWS Glue to compress the data into the Apache Parquet format
• B. Use the SageMaker batch transform feature to transform the training data into a DataFrame
• C. Transform the dataset into the Recordio protobuf format
• D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data
  

Answer: C
Explanation:
The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:
Common Data Formats for Training
Using RecordIO Format
Content Types Supported by Built-in Algorithms

NEW QUESTION # 46
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