LATEST MLS-C01 EXAM ONLINE - NEW MLS-C01 TEST DUMPS

Latest MLS-C01 Exam Online - New MLS-C01 Test Dumps

Latest MLS-C01 Exam Online - New MLS-C01 Test Dumps

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To qualify for the Amazon MLS-C01 Exam, candidates must have at least one year of experience in developing and deploying machine learning models on the AWS platform. They should have a strong understanding of machine learning algorithms, data preparation, and model optimization techniques. Additionally, candidates should be proficient in Python programming language and have experience with AWS services such as Amazon S3, AWS Lambda, and AWS CloudFormation.

Amazon AWS Certified Machine Learning Specialty Exam is a rigorous certification process that can help demonstrate a professional's expertise in designing, building, and deploying machine learning solutions on AWS. By earning this certification, candidates can position themselves as highly skilled professionals in the field of machine learning and set themselves apart from others in the field.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q13-Q18):

NEW QUESTION # 13
A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.
Which visualization will help the data scientist better understand the data trend?

  • A. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.
  • B. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.
  • C. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store.
    Add a horizontal line in each facet to represent average sales.
  • D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

Answer: B

Explanation:
Explanation
The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.
References:
pandas.DataFrame.groupby - pandas 2.1.4 documentation
pandas.DataFrame.plot.bar - pandas 2.1.4 documentation
Matplotlib - Bar Plot - Online Tutorials Library


NEW QUESTION # 14
A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

  • A. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
  • B. Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
  • C. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3.
    Configure OuickSight to access the data in Amazon S3.
  • D. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3.Configure OuickSight to access the data in Amazon S3.

Answer: C

Explanation:
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3. This solution has the following advantages:
* It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
* It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
* It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
* Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3.
* Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
* Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
References:
* 1: Amazon Kinesis Data Streams - Amazon Web Services
* 2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
* 3: AWS Glue - Amazon Web Services
* 4: Visualising your Amazon DynamoDB data with Amazon QuickSight - DEV Community
* 5: Amazon Kinesis Data Firehose - Amazon Web Services


NEW QUESTION # 15
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 accuracy?

  • A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
  • B. Single perceptron with tanh activation function
  • C. Logistic regression
  • D. Support vector machine (SVM) with non-linear kernel

Answer: C


NEW QUESTION # 16
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. Transform the dataset into the Recordio protobuf format
  • B. Use the SageMaker batch transform feature to transform the training data into a DataFrame
  • C. Use the SageMaker hyperparameter optimization feature to automatically optimize the data
  • D. Use AWS Glue to compress the data into the Apache Parquet format

Answer: A


NEW QUESTION # 17
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

  • A. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
  • B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • D. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • E. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Answer: A,E

Explanation:
Explanation
The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).
The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.
The XGBoost algorithm can also use different evaluation metrics to optimize the model performance.
The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives.
A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.
Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.
References:
XGBoost Parameters
XGBoost for Imbalanced Classification


NEW QUESTION # 18
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