Rebirth MLS-C01 Question For AWS Certified Machine Learning - Specialty Certification

Testking offers free demo for MLS-C01 exam. "AWS Certified Machine Learning - Specialty", also known as MLS-C01 exam, is a Amazon-Web-Services Certification. This set of posts, Passing the Amazon-Web-Services MLS-C01 exam, will help you answer those questions. The MLS-C01 Questions & Answers covers all the knowledge points of the real exam. 100% real Amazon-Web-Services MLS-C01 exams and revised by experts!

Free demo questions for Amazon-Web-Services MLS-C01 Exam Dumps Below:

NEW QUESTION 1
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 CloudWatc
  • B. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatc
  • D. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • E. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrai
  • F. Add code to push a custom metric to Amazon CloudWatc
  • G. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • H. 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.

Answer: C

NEW QUESTION 2
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

  • A. Associate the Amazon SageMaker notebook with a private subnet in a VP
  • B. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
  • C. Associate the Amazon SageMaker notebook with a private subnet in a VP
  • D. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
  • E. Associate the Amazon SageMaker notebook with a private subnet in a VP
  • F. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
  • G. Associate the Amazon SageMaker notebook with a private subnet in a VP
  • H. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker

Answer: D

NEW QUESTION 3
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?

  • A. Write a direct connection to the SQL database within the notebook and pull data in
  • B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
  • C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
  • D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Answer: B

NEW QUESTION 4
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been
advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

  • A. Precision
  • B. Recall
  • C. Area Under the ROC Curve (AUC)
  • D. Root Mean Square Error (RMSE)

Answer: A

NEW QUESTION 5
A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine
maintenance The engine data is loaded into a data lake for training
Which is the MOST suitable predictive model that can be deployed into production'?

  • A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • B. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
  • C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • D. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

Answer: B

NEW QUESTION 6
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions
Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

  • A. Perform part-of-speech tagging and keep the action verb and the nouns only
  • B. Normalize all words by making the sentence lowercase
  • C. Remove stop words using an English stopword dictionary.
  • D. Correct the typography on "quck" to "quick."
  • E. One-hot encode all words in the sentence
  • F. Tokenize the sentence into words.

Answer: ABD

NEW QUESTION 7
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

  • A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
  • B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
  • C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
  • D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Answer: A

NEW QUESTION 8
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?

  • A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
  • B. Ingest the data into Apache Spark Streaming using Amazon EM
  • C. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
  • D. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
  • E. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Answer: B

NEW QUESTION 9
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

  • A. Convert the records to Apache Parquet format
  • B. Convert the records to JSON format
  • C. Convert the records to GZIP CSV format
  • D. Convert the records to XML format

Answer: A

NEW QUESTION 10
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

  • A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
  • B. Use AWS Glue to catalogue the data and Amazon Athena to run queries
  • C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes
  • D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Answer: D

NEW QUESTION 11
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

  • A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
  • C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: A

NEW QUESTION 12
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3
The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3
Which solution takes the LEAST effort to implement?

  • A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
  • B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
  • C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
  • D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

Answer: C

NEW QUESTION 13
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]
MLS-C01 dumps exhibit
Considering the graph, what is a reasonable selection for the optimal choice of k?

  • A. 1
  • B. 4
  • C. 7
  • D. 10

Answer: C

NEW QUESTION 14
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create one-hot word encoding vectors.
  • B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  • C. Create word embedding factors that store edit distance with every other word.
  • D. Download word embedding’s pre-trained on a large corpus.

Answer: A

NEW QUESTION 15
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

  • A. AWSDMS
  • B. Amazon Kinesis Data Streams
  • C. Amazon Kinesis Data Firehose
  • D. Amazon Kinesis Data Analytics

Answer: C

NEW QUESTION 16
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3
The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3
Which approach will provide the information required for further analysis?

  • A. Use Amazon Comprehend with the transcribed files to build the key topics
  • B. Use Amazon Translate with the transcribed files to train and build a model for the key topics
  • C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
  • D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

Answer: B

NEW QUESTION 17
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 analysts and entity detection
  • B. Amazon SageMaker BlazingText allow mode
  • C. Natural Language Toolkit (NLTK) stemming and stop word removal
  • D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Answer: D

NEW QUESTION 18
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. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
  • B. 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.
  • C. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
  • D. Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

Answer: A

NEW QUESTION 19
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?
MLS-C01 dumps exhibit

  • A. The model might have prediction errors over a range of target values.
  • B. The dataset cannot be accurately represented using the regression model
  • C. There are too many variables in the model
  • D. The model is predicting its target values perfectly.

Answer: D

NEW QUESTION 20
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

  • A. Bundle the NVIDIA drivers with the Docker image.
  • B. Build the Docker container to be NVIDIA-Docker compatible.
  • C. Organize the Docker container's file structure to execute on GPU instances.
  • D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

Answer: A

NEW QUESTION 21
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure? Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

  • A. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
  • B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP
  • C. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
  • D. Use AWS KMS to encrypt the data on Amazon S3

Answer: C

NEW QUESTION 22
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning
The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:
• Solution simplicity
• Fast development time
• Low cost
• High flexibility
What technologies meet the company's requirements?

  • A. Amazon S3 and Amazon Athena
  • B. Amazon Redshift and AWS Glue
  • C. Amazon DynamoDB and DynamoDB Accelerator (DAX)
  • D. Amazon RDS and Amazon ES

Answer: B

NEW QUESTION 23
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