SPECIFICATIONS OF AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY PRACTICE EXAM SOFTWARE

Specifications of AWS-Certified-Machine-Learning-Specialty Practice Exam Software

Specifications of AWS-Certified-Machine-Learning-Specialty Practice Exam Software

Blog Article

Tags: Latest AWS-Certified-Machine-Learning-Specialty Exam Duration, AWS-Certified-Machine-Learning-Specialty Exam Learning, Valid Exam AWS-Certified-Machine-Learning-Specialty Book, AWS-Certified-Machine-Learning-Specialty Exam Book, AWS-Certified-Machine-Learning-Specialty Online Training Materials

What's more, part of that 2Pass4sure AWS-Certified-Machine-Learning-Specialty dumps now are free: https://drive.google.com/open?id=15gu-jSmAGCUT4gp5XoibPenug84pCTnP

We have three different versions of our AWS-Certified-Machine-Learning-Specialty exam questions which can cater to different needs of our customers. They are the versions: PDF, Software and APP online. The PDF version of our AWS-Certified-Machine-Learning-Specialty exam simulation can be printed out, suitable for you who like to take notes, your unique notes may make you more profound. The Software version of our AWS-Certified-Machine-Learning-Specialty Study Materials can simulate the real exam. Adn the APP online version can be applied to all electronic devices.

Achieving the Amazon MLS-C01 certification demonstrates your ability to solve complex machine learning problems using AWS services. AWS Certified Machine Learning - Specialty certification validates your skills in designing, developing, and deploying machine learning models on the AWS cloud platform. By obtaining this certification, you can showcase your expertise to potential employers and clients, and increase your career opportunities in the field of machine learning.

>> Latest AWS-Certified-Machine-Learning-Specialty Exam Duration <<

AWS-Certified-Machine-Learning-Specialty Exam Learning - Valid Exam AWS-Certified-Machine-Learning-Specialty Book

As for candidates who will attend the exam, choosing the practicing materials may be a difficult choice. Then just trying AWS-Certified-Machine-Learning-Specialty learning materials of us, with the pass rate is 98.95%, we help the candidates to pass the exam successfully. Many candidates have sent their thanks to us for helping them to pass the exam by using the AWS-Certified-Machine-Learning-Specialty Learning Materials. The reason why we gain popularity in the customers is the high-quality of AWS-Certified-Machine-Learning-Specialty exam dumps. In addition, we provide you with free update for one year after purchasing. Our system will send the latest version to you email address automatically.

Getting the Results

The minimum passing score for this test is 750 marks. The result will be reported on a scale of 100-1000. Note that there might be some unscored items in the exam that are not identified but don't affect your score. Also, always keep in mind that you don't need to succeed in each section to get a pass status and obtain the certification — only a total amount of points matters. The report is only needed to show individuals their performance in each domain and help them identify what are their weak and strong areas of Machine Learning.

The MLS-C01 exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection and evaluation, deep learning, and natural language processing (NLP). It also covers AWS-specific topics such as AWS SageMaker, AWS Deep Learning AMIs, AWS Machine Learning APIs, and AWS Machine Learning Services.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q281-Q286):

NEW QUESTION # 281
A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

  • A. Reduce the number of layers and units (or neurons) from the deep learning network.
  • B. Allocate a higher proportion of the overall data to the training dataset
  • C. Increase the randomization of training data in the mini-batches used in training.
  • D. Apply L1 or L2 regularization and dropouts to the training.

Answer: D

Explanation:
Regularization and dropouts are techniques that can help reduce overfitting in deep learning models. Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights. Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is less prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data. References:
Regularization: A video that explains the concept and benefits of regularization in deep learning.
Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.


NEW QUESTION # 282
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

  • A. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
  • B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
  • C. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
  • D. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Answer: A

Explanation:
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3
* AWS Machine Learning Training - Using Bucket Policies and User Policies


NEW QUESTION # 283
An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.
How should the data scientist meet these requirements MOST cost-effectively?

  • A. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
    {"HyperParameterTuningJobObjective": {"MetricName": "validation:accuracy", "Type": "Maximize"}}
  • B. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName":
    "validation:f1", "Type": "Minimize"}).
  • C. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName":
    "validation:f1", "Type": "Maximize"}}.
  • D. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
    {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.

Answer: C

Explanation:
The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on
{"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.
The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format.
This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.
Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be "Maximize" to achieve the highest F1 score.
By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.


NEW QUESTION # 284
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

  • A. Amazon Kinesis Data Firehose
  • B. AWSDMS
  • C. Amazon Kinesis Data Analytics
  • D. Amazon Kinesis Data Streams

Answer: A

Explanation:
Explanation
Amazon Kinesis Data Firehose is a service that can ingest streaming data and store it in various destinations, including Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Amazon Kinesis Data Firehose can also convert the incoming data to Apache Parquet or Apache ORC format before storing it in Amazon S3. This can reduce the storage cost and improve the performance of analytical queries on the data.
Amazon Kinesis Data Firehose supports various data sources, such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka, AWS IoT, and custom applications. Amazon Kinesis Data Firehose can also apply data transformation and compression using AWS Lambda functions.
AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or Parquet format.
Amazon Kinesis Data Streams is a service that can ingest and process streaming data in real time, but it does not store the data in any destination. Amazon Kinesis Data Streams can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format.
Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink, but it does not store the data in any destination. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Firehose to store the data in Parquet format. References:
Amazon Kinesis Data Firehose - Amazon Web Services
What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose FAQs - Amazon Web Services


NEW QUESTION # 285
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data Which of the following services can feed data to the MapReduce jobs? (Select TWO )

  • A. Amazon Athena
  • B. Amazon ES
  • C. AWSDMS
  • D. AWS Data Pipeline
  • E. Amazon Kinesis

Answer: D,E

Explanation:
Amazon Kinesis and AWS Data Pipeline are two services that can feed data to the Amazon EMR MapReduce jobs. Amazon Kinesis is a service that can ingest, process, and analyze streaming data in real time. Amazon Kinesis can be integrated with Amazon EMR to run MapReduce jobs on streaming data sources, such as web logs, social media, IoT devices, and clickstreams. Amazon Kinesis can handle data that needs to be processed in near-real time, such as for anomaly detection, fraud detection, or dashboarding. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run MapReduce jobs on batch data sources, such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon Redshift.
AWS Data Pipeline can handle data that can be moved hourly, such as for data warehousing, reporting, or machine learning.
AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or MapReduce jobs.
Amazon Athena is a service that can query data stored in Amazon S3 using standard SQL, but it does not feed data to Amazon EMR or run MapReduce jobs.
Amazon ES is a service that provides a fully managed Elasticsearch cluster, which can be used for search, analytics, and visualization, but it does not feed data to Amazon EMR or run MapReduce jobs. References:
* Using Amazon Kinesis with Amazon EMR - Amazon EMR
* AWS Data Pipeline - Amazon Web Services
* Using AWS Data Pipeline to Run Amazon EMR Jobs - AWS Data Pipeline


NEW QUESTION # 286
......

AWS-Certified-Machine-Learning-Specialty Exam Learning: https://www.2pass4sure.com/AWS-Certified-Machine-Learning/AWS-Certified-Machine-Learning-Specialty-actual-exam-braindumps.html

BONUS!!! Download part of 2Pass4sure AWS-Certified-Machine-Learning-Specialty dumps for free: https://drive.google.com/open?id=15gu-jSmAGCUT4gp5XoibPenug84pCTnP

Report this page