MLS-C01 Practice Questions

A.  

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.

B.  

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.

C.  

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

D.  

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

A.  

Amazon SageMaker to search the largest number of combinations possible

B.  

Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.

C.  

Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible

D.  

Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

A.  

A k-fold cross-validation strategy with k=5

B.  

A stratified k-fold cross-validation strategy with k=5

C.  

A k-fold cross-validation strategy with k=5 and 3 repeats

D.  

An 80/20 stratified split between training and validation

A.  

Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.

B.  

Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.

C.  

Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.

D.  

Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

A.  

Streaming classification

B.  

Binary classification

C.  

Multi-category classification

D.  

Regression classification

A.  

Change preprocessing to use n-grams.

B.  

Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.

C.  

Adjust hyperparameters related to the attention mechanism.

D.  

Choose a different weight initialization type.

A.  

Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

B.  

Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

C.  

Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.

D.  

Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.

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

A.  

Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

B.  

Tokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.

C.  

Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

D.  

Train an Amazon SageMaker Blazing Text model to generate the product categories.

A.  

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an 1AM role for data scientists to access and search through feature groups.

B.  

Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an 1AM role for data scientists to access and search through feature groups.

C.  

Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn onversioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an 1AM

D.  

Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an 1AM policy that allows data scientists to access both tables.