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[General] Excellect MLS-C01 Pass Rate, Valid MLS-C01 Exam Tips

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【General】 Excellect MLS-C01 Pass Rate, Valid MLS-C01 Exam Tips

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The AWS Certified Machine Learning - Specialty Exam is intended for individuals who have a strong understanding of machine learning, including deep learning and neural networks, and who have experience designing, implementing, and deploying machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification is particularly valuable for data scientists, software developers, and other IT professionals who want to demonstrate their expertise in machine learning and differentiate themselves in a competitive job market. With this certification, candidates can showcase their skills to potential employers and clients, as well as gain access to exclusive AWS resources and networking opportunities.
To qualify for this certification, you must have a solid understanding of the AWS platform and its machine learning services, as well as a working knowledge of programming languages such as Python, R, or Java. Additionally, you should have experience in designing, training, and deploying machine learning models using AWS services such as Amazon SageMaker, Amazon Comprehend, Amazon Rekognition, and Amazon Polly.
To be eligible to take the AWS Certified Machine Learning - Specialty certification exam, the candidate must have a minimum of one year of experience using AWS services, and must have a strong understanding of machine learning concepts and techniques. AWS Certified Machine Learning - Specialty certification exam is a combination of multiple-choice and multiple-response questions, and requires the candidate to demonstrate their practical skills by completing a hands-on lab exercise. Upon passing the exam, the candidate will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q133-Q138):NEW QUESTION # 133
A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.
Which is the FASTEST route to index the assets?
  • A. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
  • B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
  • C. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.
  • D. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
Answer: D
Explanation:
Explanation
Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe are AWS machine learning services that can analyze and extract metadata from images, text, audio, and video content. These services are easy to use, scalable, and do not require any machine learning expertise. They can help the media company to quickly index its assets and enable rapid identification of relevant content by the research team. Using these services is the fastest route to index the assets, compared to the other options that involve human intervention, custom model development, or additional steps. References:
AWS Media Intelligence Solutions
AWS Machine Learning Services
The Best Services For Running Machine Learning Models On AWS

NEW QUESTION # 134
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. Concatenate the features with high correlation scores by using a Jupyter notebook.
  • B. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
  • C. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
  • D. Drop the features with low correlation scores by using a Jupyter notebook.
Answer: C

NEW QUESTION # 135
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)
  • A. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
    "enrolled" or "not enrolled."
  • B. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
  • C. Use a regression algorithm to run predictions.
  • D. Use a forecasting algorithm to run predictions.
  • E. Use a classification algorithm to run predictions
Answer: B,E
Explanation:
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named
"enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
Use Amazon SageMaker Ground Truth to Label Data
Classification Algorithm in Machine Learning

NEW QUESTION # 136
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

  • A. Normalization transformation
  • B. Tokenization
  • C. One-hot encoding
  • D. Binarization
Answer: C
Explanation:
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
* One-Hot Encoding - Amazon SageMaker
* One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
* One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 137
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?
  • A. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
  • B. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
  • C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
  • D. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
Answer: D
Explanation:
Explanation
A precision-recall curve is a plot that shows the trade-off between the precision and recall of a binary classifier as the decision threshold is varied. It is a useful tool for evaluating and comparing the performance of different models. To generate a precision-recall curve, the following steps are needed:
Calculate the precision and recall values for different threshold values using the predictions and the true labels of the data.
Plot the precision values on the y-axis and the recall values on the x-axis for each threshold value.
Optionally, calculate the area under the curve (AUC) as a summary metric of the model performance.
Among the four options, option C requires the least coding effort to generate and share a visualization of the daily precision-recall curve from the predictions. This option involves the following steps:
Run a daily Amazon EMR workflow to generate precision-recall data: Amazon EMR is a service that allows running big data frameworks, such as Apache Spark, on a managed cluster of EC2 instances.
Amazon EMR can handle large-scale data processing and analysis, such as calculating the precision and recall values for different threshold values from 100 TB of predictions. Amazon EMR supports various languages, such as Python, Scala, and R, for writing the code to perform the calculations. Amazon EMR also supports scheduling workflows using Apache Airflow or AWS Step Functions, which can automate the daily execution of the code.
Save the results in Amazon S3: Amazon S3 is a service that provides scalable, durable, and secure object storage. Amazon S3 can store the precision-recall data generated by Amazon EMR in a cost-effective and accessible way. Amazon S3 supports various data formats, such as CSV, JSON, or Parquet, for storing the data. Amazon S3 also integrates with other AWS services, such as Amazon QuickSight, for further processing and visualization of the data.
Visualize the arrays in Amazon QuickSight: Amazon QuickSight is a service that provides fast, easy-to-use, and interactive business intelligence and data visualization. Amazon QuickSight can connect to Amazon S3 as a data source and import the precision-recall data into a dataset. Amazon QuickSight can then create a line chart to plot the precision-recall curve from the dataset. Amazon QuickSight also supports calculating the AUC and adding it as an annotation to the chart.
Publish them in a dashboard shared with the Business team: Amazon QuickSight allows creating and publishing dashboards that contain one or more visualizations from the datasets. Amazon QuickSight also allows sharing the dashboards with other users or groups within the same AWS account or across different AWS accounts. The Business team can access the dashboard with read-only permissions and view the daily precision-recall curve from the predictions.
The other options require more coding effort than option C for the following reasons:
Option A: This option requires writing code to plot the precision-recall curve from the data stored in Amazon S3, as well as creating a mechanism to share the plot with the Business team. This can involve using additional libraries or tools, such as matplotlib, seaborn, or plotly, for creating the plot, and using email, web, or cloud services, such as AWS Lambda or Amazon SNS, for sharing the plot.
Option B: This option requires transforming the predictions into a format that Amazon QuickSight can recognize and import as a data source, such as CSV, JSON, or Parquet. This can involve writing code to process and convert the predictions, as well as uploading them to a storage service, such as Amazon S3 or Amazon Redshift, that Amazon QuickSight can connect to.
Option D: This option requires writing code to generate precision-recall data in Amazon ES, as well as creating a dashboard to visualize the data. Amazon ES is a service that provides a fully managed Elasticsearch cluster, which is mainly used for search and analytics purposes. Amazon ES is not designed for generating precision-recall data, and it requires using a specific data format, such as JSON, for storing the data. Amazon ES also requires using a tool, such as Kibana, for creating and sharing the dashboard, which can involve additional configuration and customization steps.
References:
Precision-Recall
What Is Amazon EMR?
What Is Amazon S3?
[What Is Amazon QuickSight?]
[What Is Amazon Elasticsearch Service?]

NEW QUESTION # 138
......
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