We have updated the MLS-C01 exam dumps for candidates. This newly released MLS-C01 exam dumps has actual questions and answers that are the best MLS-C01 exam preparation questions for your actual exam.
Find the best online MLS-C01 exam dumps preparation questions in 2022 to start your preparation and then you’ve come to the right place. Pass4itSure MLS-C01 exam dumps https://www.pass4itsure.com/aws-certified-machine-learning-specialty.html 215 New questions and answers are waiting for you to learn.
What exactly is the Amazon MLS-C01 exam?
Successfully passing the MLS-C01 exam and earning the AWS Certified Machine Learning – Specialty certification recognizes your expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.
Amazon MLS-C01 exam method: Pearson VUE and PSI; Test centers or online proctored exams.
There is a $300 fee for the exam. You’ll need to answer 65 questions in 180 minutes. The question types are single-choice or multiple-choice.
Are exams hard? How do I pass the AWS Certified Machine Learning – Specialty exam?
Today, I’m going to share the best learning resource – Pass4itSure MLS-C01 exam dumps passed one of the toughest AWS Certified Machine Learning – Specialty (MLS-C01) certificates, and yes, your guess is correct. The MLS-C01 exam is hard to pass, but with the latest MLS-C01 exam preparation questions, it’s not hard.
What are some effective resources to prepare for the Amazon MLS-C01 exam?
Rest assured, all free resources are shared here. I understand everyone.
- AWS Certified Machine Learning – Specialty Exam Guide
- AWS Certified Machine Learning – Specialty Sample Questions
- AWS Certified Machine Learning – Specialty Official Practice Question Set
- Exam Readiness: AWS Certified Machine Learning – Specialty
- Exam Readiness: AWS Certified Machine Learning – Specialty webinar
- Process Model: CRISP-DM on the AWS Stack
- The Elements of Data Science
- Augmented AI: The Power of Human and Machine
- Machine Learning Lens – AWS Well-Architected Framework
By the way, you can try our free MLS-C01 dumps pdf: https://drive.google.com/file/d/1Ab3nfWHr6upWl4HfdZiaB2rZx-RefLTD/view?usp=share_link
Looking for free MLS-C01 exam questions to prepare?
Here is the right place where you can read the free MLS-C01 questions below.
Amazon MLS-C01 Free Dumps Updated MLS-C01 Exam Questions
Q1 – New
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?
A. Use an Amazon SageMaker notebook for both feature engineering and model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
D. Use Amazon ML for both feature engineering and model development.
Correct Answer: B
Q2 – New
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 with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
B. 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
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
Correct Answer: B
Q3 – New
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
A. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon
SNS to receive a notification when the model is overfitting.
D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a
notification when the model is overfitting.
Correct Answer: C
Q4 – New
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model\\’s performance?
A. Receiver operating characteristic (ROC) curve
B. Misclassification rate
C. Root Mean Square Error (RMand)
D. L1 norm
Correct Answer: A
Q5 – New
A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?
A. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.
B. Replace the current endpoint with a multi-model endpoint using SageMaker.
C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
D. Increase the cooldown period for the scale-out activity.
Correct Answer: A
Q6 – New
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. 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.
B. 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.
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.
Correct Answer: D
Q7 – New
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. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
C. Delete the endpoint and recreate it with the original configuration.
D. Retrain the model again by using a combination of the original training set and the new training set.
Correct Answer: B
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents. How should a Machine Learning Specialist address this issue for future documents\\’?
A. Convert current documents to SSML with pronunciation tags
B. Create an appropriate pronunciation lexicon.
C. Output speech marks to guide in pronunciation
D. Use Amazon Lex to preprocess the text files for pronunciation
Correct Answer: A
Q9 – New
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 collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
Correct Answer: B
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.
Q10 – New
A retail company uses a machine learning (ML) model for daily sales forecasting. The company\\’s brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company\\’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model\\’s inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model\\’s degradation MOST accurately?
A. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
B. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
C. Create a line chart with the weekly mean absolute error (MAE) of the model.
D. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
Correct Answer: C
Q11 – New
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. Logistic regression
B. Random Cut Forest (RCF)
C. Principal component analysis (PCA)
D. Linear regression
Correct Answer: B
Q12 – New
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews.
A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)
A. Emails exchanged by customers and the company\’s customer service agents
B. Social media posts containing the name of the company or its products
C. A publicly available collection of news articles
D. A publicly available collection of customer reviews
E. Product sales revenue figures for the company
F. Instruction manuals for the company\’s products
Correct Answer: BDF
Q13 – New
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
1. Must be accessible from a VPC only.
2. Must not traverse the public internet.
How can these requirements be satisfied?
A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
Correct Answer: B
Using the latest MLS-C01 exam dumps, especially the Pass4itSure website, you will be able to pass the MLS-C01 exam without a hitch. Click here for a full MLS-C01 exam dumps.