100% PASS AMAZON - MLS-C01 AUTHORITATIVE CERTIFICATION SAMPLE QUESTIONS

100% Pass Amazon - MLS-C01 Authoritative Certification Sample Questions

100% Pass Amazon - MLS-C01 Authoritative Certification Sample Questions

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Amazon MLS-C01 certification exam is designed for professionals who work with machine learning and want to demonstrate their expertise in this field. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, software developers, and other IT professionals who want to validate their skills and knowledge in machine learning on the AWS cloud platform.

To qualify for the AWS Certified Machine Learning - Specialty exam, candidates must have at least one year of experience in developing machine learning models on AWS and must possess a deep understanding of AWS services for data analytics, data warehousing, and data processing. MLS-C01 exam consists of 65 multiple-choice and multiple-response questions that must be completed within 180 minutes. To pass the exam, candidates must score at least 72% on the exam. Upon passing the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years. AWS Certified Machine Learning - Specialty certification is recognized globally and demonstrates an individual’s expertise in the field of machine learning on the AWS platform.

The AWS Certified Machine Learning - Specialty certification is intended for individuals who have a strong understanding of ML concepts, such as supervised and unsupervised learning, feature engineering, and deep learning. MLS-C01 Exam validates the ability to use AWS services and tools to build, train, and deploy ML models. AWS Certified Machine Learning - Specialty certification is highly valued in the industry as it demonstrates a high level of expertise in machine learning on AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q165-Q170):

NEW QUESTION # 165
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

  • A. The training channel identifying the location of training data on an Amazon S3 bucket.
  • B. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • D. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • E. Hyperparameters in a JSON array as documented for the algorithm used.
  • F. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Answer: A,C,F


NEW QUESTION # 166
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?

  • A. Amazon Comprehend syntax analysis and entity detection
  • B. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
  • C. Amazon SageMaker BlazingText cbow mode
  • D. Natural Language Toolkit (NLTK) stemming and stop word removal

Answer: B

Explanation:
https://monkeylearn.com/sentiment-analysis/


NEW QUESTION # 167
A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:
March 1st, 2020, 08:14pm -
There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.
Which solution requires the LEAST operational overhead to create a new dataset with the added features?

  • A. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
  • B. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
  • C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date
    /time transform to generate the new variables, and save the dataset as a new file in Amazon S3.
  • D. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

Answer: C

Explanation:
The solution C will create a new dataset with the added features with the least operational overhead because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution C involves the following steps:
Create a new flow in Amazon SageMaker Data Wrangler. A flow is a visual representation of the data preparation steps that can be applied to one or more datasets. The data scientist can create a new flow in the Amazon SageMaker Studio interface and import the S3 file as a data source1.
Use the Featurize date/time transform to generate the new variables. Amazon SageMaker Data Wrangler provides a set of preconfigured transformations that can be applied to the data with a few clicks. The Featurize date/time transform can parse a date/time column and generate new columns for the year, month, day, hour, minute, second, day of week, and day of year. The data scientist can use this transform to create the new variables from the timestamp variable2.
Save the dataset as a new file in Amazon S3. Amazon SageMaker Data Wrangler can export the transformed dataset as a new file in Amazon S3, or as a feature store in Amazon SageMaker Feature Store. The data scientist can choose the output format and location of the new file3.
The other options are not suitable because:
Option A: Creating an Amazon EMR cluster and developing PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the Amazon EMR cluster, the PySpark application, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing4.
Option B: Creating a processing job in Amazon SageMaker and developing Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the processing job, the Python code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing5.
Option D: Creating an AWS Glue job and developing code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the AWS Glue job, the code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing6.
1: Amazon SageMaker Data Wrangler
2: Featurize Date/Time - Amazon SageMaker Data Wrangler
3: Exporting Data - Amazon SageMaker Data Wrangler
4: Amazon EMR
5: Processing Jobs - Amazon SageMaker
6: AWS Glue


NEW QUESTION # 168
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 trend well, but not the seasonality.
  • B. The model predicts both the trend and the seasonality well.
  • C. The model does not predict the trend or the seasonality well.
  • D. The model predicts the seasonality well, but not the trend.

Answer: C


NEW QUESTION # 169
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)

  • A. Install the SageMaker Spark library in the Spark environment.
  • B. Download the AWS SDK for the Spark environment
  • C. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
  • D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
  • E. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
  • F. Use the appropriate estimator from the SageMaker Spark Library to train a model.

Answer: A,C,F

Explanation:
The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:
* Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built- in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.
* Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.
* Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.
To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:
* Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.
* Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

* Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker.
For example, to get predictions for a test DataFrame, the Specialist can use the following code:
[SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.
[SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.


NEW QUESTION # 170
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