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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services for individuals who are interested in demonstrating their proficiency in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty exam is designed for professionals who have a strong understanding of machine learning concepts and the ability to design, implement, and maintain machine learning solutions on AWS.

The Amazon AWS-Certified-Machine-Learning-Specialty Exam consists of multiple-choice questions and covers a wide range of topics related to machine learning, including data engineering, exploratory data analysis, modeling, optimization, and deployment. Successful completion of the exam demonstrates an individual's ability to design, implement, and maintain machine learning solutions on the AWS platform.

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AWS MLS-C01 Exam Certification Details:

Sample Questions
AWS MLS-C01 Sample Questions

Exam Price
$300 USD

Duration
180 minutes

Recommended Training / Books
Practical Data Science with Amazon SageMaker
The Machine Learning Pipeline on AWS
Deep Learning on AWS

Exam Name
AWS Certified Machine Learning - Specialty (Machine Learning Specialty)

Number of Questions
65

Schedule Exam
PEARSON VUE

 

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q159-Q164):

NEW QUESTION # 159
The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A. The model predicts the seasonality well, but not the trend.
• B. The model does not predict the trend or the seasonality well.
• C. The model predicts both the trend and the seasonality well.
• D. The model predicts the trend well, but not the seasonality.

Answer: B

 

NEW QUESTION # 160
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.
Which solution will result in the MOST cost savings?

• A. Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
• B. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.
• C. Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• D. Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml. r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

Answer: D

Explanation:
Explanation
The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:
Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources. For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.4xlarge instance costs $0.888 per hour1.
Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running. For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.
Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance. For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Therefore, the ml.r5.4xlarge instance can process the same amount of data in half the time and at a lower cost than the ml.m5.4xlarge instance. Moreover, using Amazon SageMaker Processing will allow the data preprocessing job to run on a separate, fully managed infrastructure that can be scaled up or down as needed, without affecting the notebook instance.
The other options are not as effective as option C for the following reasons:
Option A is not optimal because changing the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has will not reduce the cost of the notebook, since the memory optimized instances have a higher cost per vCPU than the general-purpose instances. For example, an ml.r5.4xlarge instance has 16 vCPUs and costs $1.008 per hour, while an ml.m5.4xlarge instance has 16 vCPUs and costs $0.888 per hour1. Moreover, running both data preprocessing and feature engineering development on the same instance will not take advantage of the scalability and flexibility of Amazon SageMaker Processing.
Option B is not suitable because running data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will not reduce the cost of the data preprocessing job, since the P3 instance type is optimized for GPU-based workloads and has a higher cost per GB of memory than the ml.m5 or ml.r5 instance types. For example, an ml.p3.2xlarge instance has 61 GB of memory and costs $3.06 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Moreover, the data preprocessing job does not require GPU, so using a P3 instance type will be wasteful and inefficient.
Option D is not feasible because running data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option will not reduce the cost of the data preprocessing job, since the Reserved Instance option requires a commitment to a consistent amount of usage for a period of 1 or 3 years3. However, the data preprocessing job only runs once a day on average and completes in only 2 hours, so it does not have a consistent or predictable usage pattern.
Therefore, using the Reserved Instance option will not provide any cost savings and may incur additional charges for unused capacity.
References:
Amazon SageMaker Pricing
Manage Notebook Instances - Amazon SageMaker
Amazon EC2 Pricing - Reserved Instances

 

NEW QUESTION # 161
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business dat a. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

• A. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.
• B. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.
• C. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.
• D. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.

Answer: C

Explanation:
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
References:
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function

 

NEW QUESTION # 162
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

• A. The k-means algorithm
• B. The Latent Dirichlet Allocation (LDA) algorithm
• C. The principal component analysis (PCA) algorithm
• D. The factorization machines (FM) algorithm
• E. The Random Cut Forest (RCF) algorithm

Answer: A,C

Explanation:
The agency wants to analyze the census data for population segmentation, which is a type of unsupervised learning problem that aims to group similar data points together based on their attributes. The agency can use a combination of algorithms that can perform dimensionality reduction and clustering on the data to achieve this goal.
Dimensionality reduction is a technique that reduces the number of features or variables in a dataset while preserving the essential information and relationships. Dimensionality reduction can help improve the efficiency and performance of clustering algorithms, as well as facilitate data visualization and interpretation.
One of the most common algorithms for dimensionality reduction is principal component analysis (PCA), which transforms the original features into a new set of orthogonal features called principal components that capture the maximum variance in the data. PCA can help reduce the noise and redundancy in the data and reveal the underlying structure and patterns.
Clustering is a technique that partitions the data into groups or clusters based on their similarity or distance.
Clustering can help discover the natural segments or categories in the data and understand their characteristics and differences. One of the most popular algorithms for clustering is k-means, which assigns each data point to one of k clusters based on the nearest mean or centroid. K-means can handle large and high-dimensional datasets and produce compact and spherical clusters.
Therefore, the combination of algorithms that would provide the appropriate insights for population segmentation are PCA and k-means. The agency can use PCA to reduce the dimensionality of the census data from 500 features to a smaller number of principal components that capture most of the variation in the data.
Then, the agency can use k-means to cluster the data based on the principal components and identify the segments of the population that share similar characteristics.
Amazon SageMaker Principal Component Analysis (PCA)
Amazon SageMaker K-Means Algorithm

 

NEW QUESTION # 163
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

• A. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
• B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• C. Add more deep trees to the random forest to enable the model to learn more features.
• D. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
• E. Include a copy of the samples in the test dataset in the training dataset.

Answer: B,D

 

NEW QUESTION # 164
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