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Introduction to AWS Certified Machine Learning Specialty Exam
The AWS Certified Machine Learning Exam - Special Exam (MLS-C01) is designed for people who have a development or data science function. This exam validates a candidate's ability to create, train, adapt and implement machine learning (ML) models using the AWS cloud. Validation of a candidate's ability to design, implement, implement and maintain ML solutions for certain business problems. It will validate the candidate's ability to: Select and justify the appropriate LD approach for a given business problem. Identify the appropriate AWS services to implement ML solutions. Design and implement scalable, economic, reliable and safe ML solutions.
NEW QUESTION 100
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?
- A. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
- B. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
- C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
- D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
Answer: C
Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html
NEW QUESTION 101
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?
- A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
- B. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
- C. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
- D. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
Answer: B
Explanation:
https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py
NEW QUESTION 102
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 TB of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?
- A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
- B. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.
- C. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
- D. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
Answer: B
NEW QUESTION 103
A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
- A. Random Cut Forest (RCF)
- B. Linear regression
- C. Principal component analysis (PCA)
- D. Logistic regression
Answer: B
Explanation:
https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/regression-model-insights.html
NEW QUESTION 104
A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social medi a. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?
- A. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
- B. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
- C. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
- D. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.
Answer: B
NEW QUESTION 105
A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?
- A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
- B. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
- C. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.
- D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
Answer: C
NEW QUESTION 106
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 seasonality well, but not the trend.
- 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 trend well, but not the seasonality.
Answer: C
NEW QUESTION 107
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?
- A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
- B. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- C. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- D. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
Answer: C
Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
NEW QUESTION 108
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)
- A. AWS Trusted Advisor
- B. Amazon CloudWatch
- C. AWS CloudTrail
- D. AWS Health
- E. AWS Config
Answer: B,C
NEW QUESTION 109
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
- Combine multiple data sources.
- Reuse existing PySpark logic.
- Run the solution on the existing schedule.
- Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
- B. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- D. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use.
Answer: A
Explanation:
Kinesis Data Analytics can not directly stream the input data.
NEW QUESTION 110
An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)
- A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
- B. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
- C. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
- D. Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
- E. Use Amazon Rekognition Custom Labels to train a new model.
- F. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
Answer: D,E,F
Explanation:
Reference:
https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21
https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html
NEW QUESTION 111
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)
- A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
- B. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
- C. The output path specifying where on an Amazon S3 bucket the trained model will persist.
- D. Hyperparameters in a JSON array as documented for the algorithm used.
- E. The training channel identifying the location of training data on an Amazon S3 bucket.
- F. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
Answer: B,C,E
NEW QUESTION 112
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?
- A. Add event type and event value fields to the interactions dataset in Amazon Personalize.
- B. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
- C. Use the event tracker in Amazon Personalize to include real-time user interactions.
- D. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Answer: C
NEW QUESTION 113
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data Which reconstruction approach should the Specialist use to preserve the integrity of the dataset1?
- A. Mean substitution
- B. Listwise deletion
- C. Multiple imputation
- D. Last observation carried forward
Answer: B
NEW QUESTION 114
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?
- A. Retrain the model again by using a combination of the original training set and the new training set.
- B. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
- C. Delete the endpoint and recreate it with the original configuration.
- D. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
Answer: D
NEW QUESTION 115
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?
- A. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
- B. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
- C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
- D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
Answer: A
NEW QUESTION 116
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.
How should the data scientist split the dataset into a training and test set for this use case?
- A. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
- B. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
- C. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
- D. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
Answer: D
Explanation:
https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/
NEW QUESTION 117
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
- A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
- B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
- C. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
- D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
Answer: D
NEW QUESTION 118
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
- A. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
- B. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
- C. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
- D. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
Answer: C
Explanation:
https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train- on-your-notebook-instance/
NEW QUESTION 119
An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the dat a. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.
How should a machine learning specialist meet these requirements?
- A. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
- B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
- C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
- D. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
Answer: C
NEW QUESTION 120
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What Exam Is Necessary for AWS Machine Learning – Specialty?
The only test necessary to take the AWS Machine Learning – Specialty certification has the code MLS-C This is a specialty exam and it is delivered in English, Japanese, Korean, and Simplified Chinese. Candidates can use two types of delivery methods:
- Online using a proctored exam platform.
- In testing centers;
The registration fee for the AWS MLS-C01 exam is $300. In case candidates want to enroll in doing a practice exam, they should pay another $40. MLS-C01 test includes two types of questions. Candidates will have to answer both multiple-choice and multiple-answer items. Besides, the passing score range goes from 100 to 1,000 points. A candidate will be successful only when he/she gets a minimum score of 750 points. After successfully completing this MLS-C01 exam, you will be awarded the AWS Certified Machine Learning Specialty certification. If you add this certificate to your resume and social network, your chances to get better salary offers are higher. Also, this certification is valid for three years. But once its validity expires, you will need to check the vendor's official site for recertification.
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