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Latest AWS-Certified-Machine-Learning-Specialty Test Questions, AWS-Certified-Machine-Learning-Specialty Pass4sure Pass Guide

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed to validate the candidate’s skills and knowledge in building, designing, deploying, and maintaining machine learning (ML) solutions using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification exam is ideal for professionals who are interested in pursuing a career in the field of AI and ML, or for those who want to enhance their existing skills in the field. The AWS Certified Machine Learning - Specialty certification is recognized globally and is a testament to the candidate’s expertise in the field of ML.

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To prepare for the Amazon MLS-C01 exam, candidates should have experience with machine learning, data science, and AWS services. They should also have a strong understanding of statistics, linear algebra, and calculus. Candidates can prepare for the exam by taking training courses, reading AWS documentation, and practicing with sample questions and exams.

To take the Amazon MLS-C01 exam, candidates must have a strong background in machine learning concepts, programming languages such as Python, and experience with AWS services. AWS-Certified-Machine-Learning-Specialty Exam consists of multiple-choice and multiple-answer questions and is administered online. Candidates have 170 minutes to complete the exam and must achieve a passing score of 750 out of 1000 points.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q156-Q161):

NEW QUESTION # 156
A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

A. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.
B. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
C. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
D. Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

Answer: B

Explanation:
The FindMatches transform is a machine learning transform that can identify and match similar records from different datasets, even when the records do not have a common unique identifier or exact field values. The FindMatches transform can also remove duplicate records from a single dataset. The FindMatches transform can be used with AWS Glue crawlers and jobs to process the data from various sources and store it in a data lake. The FindMatches transform can be created and managed using the AWS Glue console, API, or AWS Glue Studio.
The other options are not suitable for this use case because:
Option A: Using an AWS Lambda function to process the data and compare equal strings in the fields from the two datasets is not an efficient or scalable solution. It would require writing custom code and handling the data loading and cleansing logic. It would also not account for variations or inconsistencies in the field values, such as spelling errors, abbreviations, or missing data.
Option B: The AWS Glue SearchTables API operation is used to search for tables in the AWS Glue Data Catalog based on a set of criteria. It is not a machine learning transform that can match records across different datasets or remove duplicates. It would also require writing custom code to invoke the API and process the results.
Option D: AWS Lake Formation does not provide a custom transform feature. It provides predefined blueprints for common data ingestion scenarios, such as database snapshot, incremental database, and log file. These blueprints do not support matching records across different datasets or removing duplicates.

NEW QUESTION # 157
Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A. Have Amazon ML split the data sequentially.
B. Have Amazon ML split the data randomly.
C. Pre-split the data before uploading to Amazon S3
D. Perform custom cross-validation on the data

Answer: A

NEW QUESTION # 158
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
C. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
D. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

Answer: D

Explanation:
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

NEW QUESTION # 159
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

A. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
C. Average colors by channel to obtain three-dimensional representations of images.
D. Reduce image resolution and use reduced resolution pixel values as features

Answer: A

Explanation:
A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower- dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.
References:
* ImageNet - Wikipedia
* How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium
* Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science

NEW QUESTION # 160
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 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.
D. 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.

Answer: D

Explanation:
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.
References:
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 # 161
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