HOT AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TEST ANSWERS | HIGH-QUALITY AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM TOPICS: AWS CERTIFIED MACHINE LEARNING - SPECIALTY 100% PASS

Hot AWS-Certified-Machine-Learning-Specialty Test Answers | High-quality AWS-Certified-Machine-Learning-Specialty Exam Topics: AWS Certified Machine Learning - Specialty 100% Pass

Hot AWS-Certified-Machine-Learning-Specialty Test Answers | High-quality AWS-Certified-Machine-Learning-Specialty Exam Topics: AWS Certified Machine Learning - Specialty 100% Pass

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AWS Certified Machine Learning Specialty Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our AWS Certified Machine Learning Specialty exam dumps will include the following topics:

  • Domain 4: Machine Learning Implementation and Operations 20%
  • Domain 1: Data Engineering 20%
  • Domain 3: Modeling 36%
  • Domain 2: Exploratory Data Analysis 24%

Recommended Experience

Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.

>> AWS-Certified-Machine-Learning-Specialty Test Answers <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q185-Q190):

NEW QUESTION # 185
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. Normalization
  • B. One-hot encoding
  • C. Quantile binning
  • D. Cartesian product transformation

Answer: C

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 # 186
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?
Choose the maximum number of hyperparameters supported by

  • A. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
  • B. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments
  • C. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
  • D. Amazon SageMaker to search the largest number of combinations possible

Answer: A

Explanation:
Explanation
Using log-scaled hyperparameters is a guideline that can improve the automatic model tuning in Amazon SageMaker. Log-scaled hyperparameters are hyperparameters that have values that span several orders of magnitude, such as learning rate, regularization parameter, or number of hidden units. Log-scaled hyperparameters can be specified by using a log-uniform distribution, which assigns equal probability to each order of magnitude within a range. For example, a log-uniform distribution between 0.001 and 1000 can sample values such as 0.001, 0.01, 0.1, 1, 10, 100, or 1000 with equal probability. Using log-scaled hyperparameters can allow the hyperparameter optimization feature to search the hyperparameter space more efficiently and effectively, as it can explore different scales of values and avoid sampling values that are too small or too large. Using log-scaled hyperparameters can also help avoid numerical issues, such as underflow or overflow, that may occur when using linear-scaled hyperparameters. Using log-scaled hyperparameters can be done by setting the ScalingType parameter to Logarithmic when defining the hyperparameter ranges in Amazon SageMaker12 The other options are not valid or relevant guidelines for improving the automatic model tuning in Amazon SageMaker. Choosing the maximum number of hyperparameters supported by Amazon SageMaker to search the largest number of combinations possible is not a good practice, as it can increase the time and cost of the tuning job and make it harder to find the optimal values. Amazon SageMaker supports up to 20 hyperparameters for tuning, but it is recommended to choose only the most important and influential hyperparameters for the model and algorithm, and use default or fixed values for the rest3 Specifying a very large hyperparameter range to allow Amazon SageMaker to cover every possible value is not a good practice, as it can result in sampling values that are irrelevant or impractical for the model and algorithm, and waste the tuning budget. It is recommended to specify a reasonable and realistic hyperparameter range based on the prior knowledge and experience of the model and algorithm, and use the results of the tuning job to refine the range if needed4 Executing only one hyperparameter tuning job at a time and improving tuning through successive rounds of experiments is not a good practice, as it can limit the exploration and exploitation of the hyperparameter space and make the tuning process slower and less efficient. It is recommended to use parallelism and concurrency to run multiple training jobs simultaneously and leverage the Bayesian optimization algorithm that Amazon SageMaker uses to guide the search for the best hyperparameter values5


NEW QUESTION # 187
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

  • A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
  • B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and tram using the full dataset.
  • C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
  • D. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode

Answer: B


NEW QUESTION # 188
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

  • A. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
  • B. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
  • C. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
  • D. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Answer: D

Explanation:
https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train- on-your-notebook-instance/


NEW QUESTION # 189
A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.
Which TensorFlow estimator configuration will train the model MOST cost-effectively?

  • A. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • B. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • C. Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • D. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer: D

Explanation:
The TensorFlow estimator configuration that will train the model most cost-effectively is to turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter, turn on managed spot training by setting the use_spot_instances parameter to True, and pass the script to the estimator in the call to the TensorFlow fit() method. This configuration will optimize the model for the target hardware platform, reduce the training cost by using Amazon EC2 Spot Instances, and use the custom Python script without any modification.
SageMaker Training Compiler is a feature of Amazon SageMaker that enables you to optimize your TensorFlow, PyTorch, and MXNet models for inference on a variety of target hardware platforms. SageMaker Training Compiler can improve the inference performance and reduce the inference cost of your models by applying various compilation techniques, such as operator fusion, quantization, pruning, and graph optimization. You can enable SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter to the TensorFlow estimator constructor1.
Managed spot training is another feature of Amazon SageMaker that enables you to use Amazon EC2 Spot Instances for training your machine learning models. Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS Cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various fault-tolerant and flexible applications. You can enable managed spot training by setting the use_spot_instances parameter to True and specifying the max_wait and max_run parameters in the TensorFlow estimator constructor2.
The TensorFlow estimator is a class in the SageMaker Python SDK that allows you to train and deploy TensorFlow models on SageMaker. You can use the TensorFlow estimator to run your own Python script on SageMaker, without any modification. You can pass the script to the estimator in the call to the TensorFlow fit() method, along with the location of your input data. The fit() method starts a SageMaker training job and runs your script as the entry point in the training containers3.
The other options are either less cost-effective or more complex to implement. Adjusting the training script to use distributed data parallelism would require modifying the script and specifying appropriate values for the distribution parameter, which could increase the development time and complexity. Setting the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter would not reduce the cost, as it would only specify the maximum duration of the training job, regardless of the instance type.
References:
1: Optimize TensorFlow, PyTorch, and MXNet models for deployment using Amazon SageMaker Training Compiler | AWS Machine Learning Blog
2: Managed Spot Training: Save Up to 90% On Your Amazon SageMaker Training Jobs | AWS Machine Learning Blog
3: sagemaker.tensorflow - sagemaker 2.66.0 documentation


NEW QUESTION # 190
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It is similar to the AWS-Certified-Machine-Learning-Specialty desktop-based software, with all the elements of the desktop practice exam. This AWS-Certified-Machine-Learning-Specialty exam can be accessed from any browser and does not require installation. The AWS-Certified-Machine-Learning-Specialty questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based AWS-Certified-Machine-Learning-Specialty Practice Test immediately through any operating system and browsers.

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