Amazon MLS-C01 Dumps - AWS Certified Machine Learning - Specialty PDF Sample Questions

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Exam Code:
MLS-C01
Exam Name:
AWS Certified Machine Learning - Specialty
281 Questions
Last Update Date : 21 May, 2024
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Realexamdumps Providing most updated AWS Certified Specialty Question Answers. Here are a few exams:


Sample Questions

Realexamdumps Providing most updated AWS Certified Specialty Question Answers. Here are a few sample questions:

Amazon MLS-C01 Sample Question 1

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to

implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used

downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use,

if needed.

The solution needs to do the following:

  • Calculate an anomaly score for each web traffic entry.
  • Adapt unusual event identification to changing web patterns over time.

Which approach should the data scientist implement to meet these requirements?


Options:

A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker RandomCut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web trafficdata. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF modelto calculate the anomaly score for each record.
B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-inXGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach apreprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model tocalculate the anomaly score for each record.
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming datawith the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using atumbling window.
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming datawith the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each recordusing a sliding window.

Answer: E

Amazon MLS-C01 Sample Question 2

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?


Options:

A. Run self-correlation on all features and remove highly correlated features
B. Normalize all numerical values to be between 0 and 1
C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Answer: C

Amazon MLS-C01 Sample Question 3

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?


Options:

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.

Answer: C

Amazon MLS-C01 Sample Question 4

A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company’s data currently resides on premises and is 40 ТВ in size.

The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.

Which solution meets these requirements?


Options:

A. Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
B. Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
D. Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

Answer: C Explanation: Explanation: Configure DataSync to make an initial copy of your entire dataset, and schedule subsequent incremental transfers of changing data until the final cut-over from on-premises to AWS.Reference: [Reference: https://aws.amazon.com/datasync/faqs/, ]

Amazon MLS-C01 Sample Question 5

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?


Options:

A. 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.
B. 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.
C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
D. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Answer: D Explanation: Reference: [Reference: https://aws.amazon.com/lake-formation/features/, ]

Amazon MLS-C01 Sample Question 6

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local

machine, and the Specialist now wants to deploy it to production for inference only.

What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?


Options:

A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname andupload it to Amazon ECR.
B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with theregistry hostname and upload it to Amazon S3.
C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it toDocker Hub.
D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to AmazonECR.

Answer: E

Amazon MLS-C01 Sample Question 7

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will

have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot

Instances in the EMR cluster.

Which nodes should the Specialist launch on Spot Instances?


Options:

A. Master node
B. Any of the core nodes
C. Any of the task nodes
D. Both core and task nodes

Answer: B

Amazon MLS-C01 Sample Question 8

A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?


Options:

A. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
B. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
C. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
D. Run a SageMaker training job with an EFS file system as the data source.

Answer: A Explanation: Reference: [Reference: https://aws.amazon.com/about-aws/whats-new/2019/08/amazon-sagemaker-works-with-amazon-fsx-lustre-amazon-efs-model-training/, ]

Amazon MLS-C01 Sample Question 9

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?


Options:

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. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
D. 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.

Answer: B Explanation: Explanation: https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.pz

Amazon MLS-C01 Sample Question 10

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?


Options:

A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
B. Drop the features with low correlation scores by using a Jupyter notebook.
C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
D. Concatenate the features with high correlation scores by using a Jupyter notebook.

Answer: D

Amazon MLS-C01 Sample Question 11

A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces

Which combination of services can be used to build this conversational interface? (Select THREE )


Options:

A. Alexa for Business
B. Amazon Connect
C. Amazon Lex
D. Amazon Poly
E. Amazon Comprehend
F. Amazon Transcribe

Answer: B, E, G

Amazon MLS-C01 Sample Question 12

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?


Options:

A. An Amazon EBS-backed Amazon EC2 instance with hourly directories
B. An Amazon RDS database with hourly table partitions
C. An Amazon S3 data lake with hourly object prefixes
D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Answer: D

Amazon MLS-C01 Sample Question 13

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?


Options:

A. Drop all records from the dataset where age has been set to 0.
B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
C. Drop the age feature from the dataset and train the model using the rest of the features.
D. Use k-means clustering to handle missing features.

Answer: B

Amazon MLS-C01 Sample Question 14

A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.

Which action will provide the MOST secure protection?


Options:

A. Remove Amazon S3 access permissions from the SageMaker execution role.
B. Encrypt the weights of the CNN model.
C. Encrypt the training and validation dataset.
D. Enable network isolation for training jobs.

Answer: E

Amazon MLS-C01 Sample Question 15

A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .

The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed

What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?


Options:

A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
B. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Answer: B

Amazon MLS-C01 Sample Question 16

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?


Options:

A. m5 4xlarge (general purpose)
B. r5.2xlarge (memory optimized)
C. p3.2xlarge (GPU accelerated computing)
D. p3 8xlarge (GPU accelerated computing)

Answer: D

Amazon MLS-C01 Sample Question 17

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.

Which implementation will meet these requirements?


Options:

A. Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
B. Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.
C. Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
D. Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

Answer: D

Amazon MLS-C01 Sample 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?


Options:

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: E

Amazon MLS-C01 Sample Question 19

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to

index its assets to allow rapid identification of relevant content by the Research team. The company wants to

use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning

expertise.

Which is the FASTEST route to index the assets?


Options:

A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinctcategories/classes.
B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
C. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM)and Object Detection algorithms to tag data into distinct categories/classes.
D. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audiotranscription and topic modeling, and use object detection to tag data into distinct categories/classes.

Answer: B

Amazon MLS-C01 Sample Question 20

A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance

How should the records be stored in Amazon S3 to improve query performance?


Options:

A. CSV files
B. Parquet files
C. Compressed JSON
D. RecordIO

Answer: E

Amazon MLS-C01 Sample Question 21

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?


Options:

A. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
D. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: D


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