Valid MLS-C01 Valid Exam Cram & Leader in Qualification Exams & Fantastic Amazon AWS Certified Machine Learning - Specialty

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by VCEEngine: https://drive.google.com/open?id=1Wyx8AtKJBpxbxVKWXHFlT0lhY-jcdqjT

If I tell you, you can get international certification by using MLS-C01 preparation materials for twenty to thirty hours. You must be very surprised. However, you must believe that this is true! You can ask anyone who has used MLS-C01 Actual Exam. We can receive numerous warm feedbacks every day. Our reputation is really good. After you have learned about the achievements of MLS-C01 study questions, you will definitely choose us!

Amazon MLS-C01 exam covers a range of topics such as data engineering, exploratory data analysis, feature engineering, model selection and training, tuning and optimization, machine learning algorithms, and deploying and maintaining machine learning models. Candidates are expected to have a deep understanding of these topics and be able to apply their knowledge to real-world scenarios. MLS-C01 Exam is designed to test the candidate's ability to create efficient and effective machine learning solutions on AWS. Successful candidates will be able to demonstrate their expertise in the field and show that they are capable of designing and deploying machine learning models that meet business requirements.

>> MLS-C01 Valid Exam Cram <<

High Hit Rate MLS-C01 Valid Exam Cram, Ensure to pass the MLS-C01 Exam

There is no doubt that if you pass the MLS-C01 exam certification test, which means that your ability and professional knowledge are acknowledged by the authority field, we suggest that you can try our MLS-C01 reliable exam dumps. Although it is difficult to prepare the exam for most people, as long as you are attempting our MLS-C01 Exam Dumps, you will find that it is not as hard as you think. What you will never worry about is that the quality of MLS-C01 exam dumps, because once you haven’t passed exam, we will have a 100% money back guarantee. You can easily pass the exam only if you spend some spare time studying our MLS-C01 materials.

Amazon MLS-C01 exam consists of multiple-choice and multiple-response questions that test an individual's ability to analyze and solve real-world machine learning problems. MLS-C01 exam covers a range of topics such as data exploration, feature engineering, model selection, and optimization. MLS-C01 Exam also tests an individual's knowledge of AWS services such as Amazon SageMaker, Amazon Comprehend, and Amazon Rekognition.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q15-Q20):

NEW QUESTION # 15
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

A. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.
B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
C. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
D. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

Answer: D

Explanation:
https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html

NEW QUESTION # 16
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 Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
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 an API call from OuickSight to access the data that is in Amazon DynamoDB directly

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 # 17
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

Explanation:
The best feature engineering strategy for this scenario is to apply dimensionality reduction by using the principal component analysis (PCA) algorithm. PCA is a technique that transforms a large set of correlated features into a smaller set of uncorrelated features called principal components. This can help reduce the complexity and noise in the data, improve the performance and interpretability of the model, and avoid overfitting. Amazon SageMaker provides a built-in PCA algorithm that can be used to perform dimensionality reduction on tabular data. The ML specialist can use Amazon SageMaker to train and deploy the PCA model, and then use the output of the PCA model as the input for the classification model.
References:
Dimensionality Reduction with Amazon SageMaker
Amazon SageMaker PCA Algorithm

NEW QUESTION # 18
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Disable root access on the SageMaker notebook instances.
B. Restrict notebook presigned URLs to specific IPs used by the company.
C. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.
D. Use SCPs to restrict access to SageMaker.
E. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
F. Enable network isolation for training jobs and models.

Answer: C,E,F

Explanation:
To control data egress from SageMaker, the ML engineer can use the following mechanisms:
* Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1
* Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2
* Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker:
* Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4
* Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
* Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.
1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
2: Network Isolation - Amazon SageMaker
3: Encrypt Data at Rest and in Transit - Amazon SageMaker
4: Using Service Control Policies - AWS Organizations
Disable Root Access - Amazon SageMaker
Create a Presigned Notebook Instance URL - Amazon SageMaker

NEW QUESTION # 19
A company wants to create an artificial intelligence (Al) yoga instructor that can lead large classes of students. The company needs to create a feature that can accurately count the number of students who are in a class. The company also needs a feature that can differentiate students who are performing a yoga stretch correctly from students who are performing a stretch incorrectly.
...etermine whether students are performing a stretch correctly, the solution needs to measure the location and angle of each student's arms and legs A data scientist must use Amazon SageMaker to ...ss video footage of a yoga class by extracting image frames and applying computer vision models.
Which combination of models will meet these requirements with the LEAST effort? (Select TWO.)

A. Image Classification
B. Pose estimation
C. Object Detection
D. Image Generative Adversarial Networks (GANs)
E. Optical Character Recognition (OCR)

Answer: B,C

Explanation:
To count the number of students who are in a class, the solution needs to detect and locate each student in the video frame. Object detection is a computer vision model that can identify and locate multiple objects in an image. To differentiate students who are performing a stretch correctly from students who are performing a stretch incorrectly, the solution needs to measure the location and angle of each student's arms and legs. Pose estimation is a computer vision model that can estimate the pose of a person by detecting the position and orientation of key body parts. Image classification, OCR, and image GANs are not relevant for this use case. References:
Object Detection: A computer vision technique that identifies and locates objects within an image or video.
Pose Estimation: A computer vision technique that estimates the pose of a person by detecting the position and orientation of key body parts.
Amazon SageMaker: A fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

NEW QUESTION # 20
......

MLS-C01 Valid Test Pdf: https://www.vceengine.com/MLS-C01-vce-test-engine.html

DOWNLOAD the newest VCEEngine MLS-C01 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1Wyx8AtKJBpxbxVKWXHFlT0lhY-jcdqjT