[Jun-2025] AWS Certified Specialty MLS-C01 Exam Practice Test Questions Dumps Bundle! [Q126-Q146]

[Jun-2025] AWS Certified Specialty MLS-C01 Exam Practice Test Questions Dumps Bundle!

2025 Updated MLS-C01 PDF for the MLS-C01 Tests Free Updated Today!

The AWS Certified Machine Learning - Specialty exam (MLS-C01) is a certification offered by Amazon Web Services (AWS) for individuals who want to validate their expertise in machine learning on the AWS cloud. AWS Certified Machine Learning - Specialty certification is designed to validate a candidate's understanding of the core concepts and best practices of machine learning implementation on AWS, including data preparation and cleaning, feature engineering, model development, and deployment.

The AWS Certified Machine Learning - Specialty Exam covers a wide range of topics related to machine learning, including data preparation and feature engineering, model selection and evaluation, training and tuning models, and deploying and managing machine learning models in production environments. MLS-C01 exam also focuses on AWS-specific machine learning services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

 

NEW QUESTION # 126
A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

• A. Accuracy
• B. False positive rate
• C. True positive rate
• D. Area under the precision-recall curve
• E. Specificity

Answer: C,D

Explanation:
Explanation
The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).
The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.
The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.
References:
Metrics for Imbalanced Classification in Python - Machine Learning Mastery Precision-Recall - scikit-learn

NEW QUESTION # 127
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 catalog; 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-real-time 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 # 128
A Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

• A. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
• B. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
• C. The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model
• D. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

Answer: D

Explanation:
The model in Experiment 1 had a high variance error because it performed well on the training data (train error = 5%) but poorly on the test data (test error = 8%). This indicates that the model was overfitting the training data and not generalizing well to new data. The model in Experiment 3 had a lower variance error because it performed similarly on the training data (train error = 5.1%) and the test data (test error = 5.4%). This indicates that the model was more robust and less sensitive to the fluctuations in the training data. The model in Experiment 3 achieved this improvement by implementing regularization, which is a technique that reduces the complexity of the model and prevents overfitting by adding a penalty term to the loss function. The model in Experiment 2 had a minimal bias error because it performed similarly on the training data (train error = 5.2%) and the test data (test error = 5.7%) as the model in Experiment 1. This indicates that the model was not underfitting the data and capturing the true relationship between the input and output variables. The model in Experiment 2 increased the number of layers and neurons in the model, which is a way to increase the complexity and flexibility of the model. However, this did not improve the performance of the model, as the variance error remained high. This shows that increasing the complexity of the model is not always the best way to reduce the bias error, and may even increase the variance error if the model becomes too complex for the data. References:
Bias Variance Tradeoff - Clearly Explained - Machine Learning Plus
The Bias-Variance Trade-off in Machine Learning - Stack Abuse

NEW QUESTION # 129
A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.
A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.
Which AWS service or feature will meet these requirements with the LEAST development effort over time?

• A. Amazon Athena
• B. Amazon EMR cluster
• C. AWS Glue jobs
• D. AWS Lambda

Answer: C

Explanation:
AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating data. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.
The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.
1: AWS Glue - Fully Managed ETL Service - Amazon Web Services
2: Amazon EMR - Amazon Web Services
3: Amazon Athena - Interactive SQL Queries for Data in Amazon S3
[4]: AWS Lambda - Serverless Compute - Amazon Web Services

NEW QUESTION # 130
For the given confusion matrix, what is the recall and precision of the model?

• A. Recall = 0.8 Precision = 0.92
• B. Recall = 0.92 Precision = 0.84
• C. Recall = 0.84 Precision = 0.8
• D. Recall = 0.92 Precision = 0.8

Answer: B

NEW QUESTION # 131
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. Build the Docker container to be NVIDIA-Docker compatible.
• B. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
• C. Organize the Docker container's file structure to execute on GPU instances.
• D. Bundle the NVIDIA drivers with the Docker image.

Answer: A

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible.
Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image.
For more information about nvidia-docker, see NVIDIA/nvidia-docker.

NEW QUESTION # 132
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 0
• B. 1
• C. 2
• D. 3

Answer: A

Explanation:
Explanation
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

NEW QUESTION # 133
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

• A. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
• B. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• C. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• D. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Answer: B

Explanation:
Explanation
To create a serverless ingestion and analytics solution for high-velocity, real-time streaming data, the Data Scientist should use the following AWS services:
AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The Data Scientist can use AWS Glue Data Catalog to create a schema of the incoming data format, which defines the structure, format, and data types of the JSON records. The schema can be used by other AWS services to understand and process the data1.
Amazon Kinesis Data Firehose: This is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. The Data Scientist can use Amazon Kinesis Data Firehose to stream the data from the source and transform the data to a query-optimized, columnar format such as Apache Parquet or ORC using the AWS Glue Data Catalog before delivering to Amazon S3. This enables efficient compression, partitioning, and fast analytics on the data2.
Amazon S3: This is an object storage service that offers high durability, availability, and scalability. The Data Scientist can use Amazon S3 as the output datastore for the transformed data, which can be organized into buckets and prefixes according to the desired partitioning scheme. Amazon S3 also integrates with other AWS services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum for analytics3.
Amazon Athena: This is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. The Data Scientist can use Amazon Athena to run SQL queries against the data in Amazon S3 and connect to existing business intelligence dashboards using the Athena Java Database Connectivity (JDBC) connector. Amazon Athena leverages the AWS Glue Data Catalog to access the schema information and supports formats such as Parquet and ORC for fast and cost-effective queries4.
References:
1: What Is the AWS Glue Data Catalog? - AWS Glue
2: What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose
3: What Is Amazon S3? - Amazon Simple Storage Service
4: What Is Amazon Athena? - Amazon Athena

NEW QUESTION # 134
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

• A. Normalization transformation
• B. One-hot encoding
• C. Binarization
• D. Tokenization

Answer: B

Explanation:
Explanation
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
One-Hot Encoding - Amazon SageMaker
One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 135
For the given confusion matrix, what is the recall and precision of the model?

• A. Recall = 0.8 Precision = 0.92
• B. Recall = 0.92 Precision = 0.84
• C. Recall = 0.84 Precision = 0.8
• D. Recall = 0.92 Precision = 0.8

Answer: D

Explanation:
Recall and precision are two metrics that can be used to evaluate the performance of a classification model.
Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:
* Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
* Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8
Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

NEW QUESTION # 136
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

• A. Increase the number of S3 prefixes for the delivery stream to write to.
• B. Increase the number of shards for the data stream.
• C. Decrease the retention period for the data stream.
• D. Add more consumers using the Kinesis Client Library (KCL).

Answer: B

Explanation:
The data ingestion rate into Amazon S3 can be improved by increasing the number of shards for the data stream. A shard is the base throughput unit of a Kinesis data stream. One shard provides 1 MB/second data input and 2 MB/second data output. Increasing the number of shards increases the data ingestion capacity of the stream. This can help reduce the backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
References:
Shard - Amazon Kinesis Data Streams
Scaling Amazon Kinesis Data Streams with AWS CloudFormation - AWS Big Data Blog

NEW QUESTION # 137
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. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
• B. Bundle the NVIDIA drivers with the Docker image.
• C. Organize the Docker container's file structure to execute on GPU instances.
• D. Build the Docker container to be NVIDIA-Docker compatible.

Answer: B

NEW QUESTION # 138
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.
What should the data scientist do to identify and address training issues with the LEAST development effort?

• A. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
• B. Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
• C. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.
• D. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.

Answer: A

Explanation:
The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:
Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.
Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.
The other options are not suitable because:
Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.
Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.
Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.
References:
1: Amazon SageMaker Debugger
2: Built-in Rules for Amazon SageMaker Debugger
3: Actions for Amazon SageMaker Debugger
4: Amazon CloudWatch Alarms
5: Amazon CloudWatch Custom Metrics

NEW QUESTION # 139
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.
Which solution will result in the MOST cost savings?

• A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.
• B. Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• C. Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
• D. Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

Answer: B

NEW QUESTION # 140
A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

• A. Apply integer transformation and set Red = 1, White = 5, and Green = 10.
• B. Replace the color name string by its length.
• C. Add new columns that store one-hot representation of colors.
• D. Replace each color name by its training set frequency.
• E. Create three columns to encode the color in RGB format.

Answer: C,E

Explanation:
In this scenario, the specialist should use one-hot encoding and RGB encoding to allow the regression model to learn from the Wall_Color data. One-hot encoding is a technique used to convert categorical data into numerical data. It creates new columns that store one-hot representation of colors. For example, a variable named color has three categories: red, green, and blue. After one-hot encoding, the new variables should be like this:

One-hot encoding can capture the presence or absence of a color, but it cannot capture the intensity or hue of a color. RGB encoding is a technique used to represent colors in a digital image. It creates three columns to encode the color in RGB format. For example, a variable named color has three categories: red, green, and blue. After RGB encoding, the new variables should be like this:

RGB encoding can capture the intensity and hue of a color, but it may also introduce correlation among the three columns. Therefore, using both one-hot encoding and RGB encoding can provide more information to the regression model than using either one alone.
References:
Feature Engineering for Categorical Data
How to Perform Feature Selection with Categorical Data

NEW QUESTION # 141
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?

• A. Ensure that the input mode for the training job is set to Pipe.
• B. Use the SageMaker batch transform feature
• C. Compress the training data into Apache Parquet format.
• D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Answer: A

Explanation:
Explanation
The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.
References:
Access Training Data - Amazon SageMaker
Choosing Data Input Mode Using the SageMaker Python SDK - Amazon SageMaker CreateTrainingJob - Amazon SageMaker Service

NEW QUESTION # 142
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 Config
• B. AWS CloudTrail
• C. AWS Trusted Advisor
• D. Amazon CloudWatch
• E. AWS Health

Answer: B,E

NEW QUESTION # 143
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 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.
• B. 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.
• C. 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.
• 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:
Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:
* How Amazon Forecast Works - Amazon Forecast
* Choosing Datasets - Amazon Forecast

NEW QUESTION # 144
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?

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

Answer: B

NEW QUESTION # 145
An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.
A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark.
The data scientist wants AWS to be responsible for the operational maintenance of the container.
Which solution will meet these requirements?

• A. Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).
• B. Package the code by extending an Amazon SageMaker scikit-learn container.
• C. Package the code into a custom-built container. Push the container to AWS Fargate.
• D. Package the code as the training script for an Amazon SageMaker scikit-learn container.

Answer: B

Explanation:
To compare a custom scikit-learn model with a benchmark model in a managed environment, the most effective and maintainable solution is to extend an existing SageMaker scikit-learn container.
"If you are using a framework like scikit-learn and need custom logic, you can extend the prebuilt SageMaker containers to include your own inference or training scripts. AWS manages the container base and you only maintain your code." This approach allows the data scientist to maintain control over model logic while letting SageMaker handle the container lifecycle, scaling, and infrastructure management-which aligns with the requirement for AWS to handle operational maintenance.

NEW QUESTION # 146
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