Which solution will meet these requirements with the LEAST operational overhead?
A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.
B. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.
D. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
Explanation:
Scenario:The ML engineer needs a low-overhead solution to query thousands of existing
and new CSV objects stored in Amazon S3 based on a transaction date.
Why Athena?
Serverless:Amazon Athena is a serverless query service that allows direct
querying of data stored in S3 using standard SQL, reducing operational overhead.
Ease of Use:By using the CTAS statement, the engineer can create a table with
optimized partitions based on the transaction date. Partitioning improves query
performance and minimizes costs by scanning only relevant data.
Low Operational Overhead:No need to manage or provision additional
infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table
creation and optimization.
Steps to Implement:
Organize Data in S3:Store CSV files in a bucket in a consistent format and
directory structure if possible.
Configure Athena:Use the AWS Management Console or Athena CLI to set up
Athena to point to the S3 bucket.
Run CTAS Statement:
CREATE TABLE processed_data
WITH (
format = 'PARQUET',
external_location = 's3://processed-bucket/',
partitioned_by = ARRAY['transaction_date']
) AS
SELECT *
FROM input_data;
This creates a new table with data partitioned by transaction date.
Query the Data:Use standard SQL queries to fetch data based on the transaction
date.
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw
data consists of millions of rows of user activity every day. ML engineers access the data to
develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3
days by using Amazon Athena. The company must retain the data for 30 days before
archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?
Explanation: Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost-effective data retention and storage management while maintaining high performance for recent data retrieval.
A company needs to host a custom ML model to perform forecast analysis. The forecast
analysis will occur with predictable and sustained load during the same 2-hour period every
day.
Multiple invocations during the analysis period will require quick responses. The company
needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?
A. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
C. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.
Explanation: SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.
A company runs an Amazon SageMaker domain in a public subnet of a newly created
VPC. The network is configured properly, and ML engineers can access the SageMaker
domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP
address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?
A. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
B. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
C. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
D. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
Explanation: Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.
A company uses Amazon SageMaker Studio to develop an ML model. The company has a
single SageMaker Studio domain. An ML engineer needs to implement a solution that
provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
A. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
B. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.
C. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
D. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
Explanation:
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of
costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making
it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost
management tools.
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset
includes transaction logs, customer profiles, and tables from an on-premises MySQL
database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm.
Additionally, many of the features have interdependencies. The algorithm is not capturing
all the desired underlying patterns in the data.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?
A. LightGBM
B. Linear learner
C. -means clustering
D. Neural Topic Model (NTM)
Explanation:
Why Linear Learner?
SageMaker'sLinear Learneralgorithm is well-suited for binary classification
problems such as fraud detection. It handles class imbalance effectively by
incorporating built-in options forweight balancingacross classes.
Linear Learner can capture patterns in the data while being computationally
efficient.
Key Features of Linear Learner:
Automatically weights minority and majority classes.
Supports both classification and regression tasks.
Handles interdependencies among features effectively through gradient
optimization.
Steps to Implement:
Use the SageMaker Python SDK to set up a training job with the Linear Learner
algorithm.
Configure the hyperparameters to enable balanced class weights.
Train the model with the balanced dataset created using SageMaker Data
Wrangler.
A company is building a deep learning model on Amazon SageMaker. The company uses
a large amount of data as the training dataset. The company needs to optimize the model's
hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST
computation time?
A. Hyperbaric!
B. Grid search
C. Bayesian optimization
D. Random search
Explanation: Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.
A company is planning to create several ML prediction models. The training data is stored
in Amazon S3. The entire dataset is more than 5 in size and consists of CSV, JSON,
Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex
manipulations that can take hours to finish running. Some of the processing involves
natural language processing (NLP) transformations. The entire process must be
automated.
Which solution will meet these requirements?
A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
B. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large
datasets and can integrate with custom steps, such as NLP transformations. By combining
SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and
automated efficiently, meeting the requirements for scalability, automation, and processing
complexity.
A company has a team of data scientists who use Amazon SageMaker notebook instances
to test ML models. When the data scientists need new permissions, the company attaches
the permissions to each individual role that was created during the creation of the
SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?
A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
D. Create a single IAM group. Add the data scientists to the group. Create an IAM role.
Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role
with the group. Associate the group with each notebook instance that the team uses.
Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become
complex when handled individually. To centralize and streamline permission management,
AWS recommends creating a single IAM role with the necessary permissions and attaching
this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
Create a Single IAM Role with Necessary Permissions:
Attach the IAM Role to Each Notebook Instance:
Benefits of This Approach:
Centralized Permission Management:By using a single IAM role, you simplify the
process of updating permissions. Changes to the role's policies automatically
propagate to all associated notebook instances, ensuring consistent access
control.
Adherence to Best Practices:AWS recommends using IAM roles to manage
permissions for applications running on services like SageMaker. This approach
avoids the need to manage individual user permissions separately.(IAM Best
Practices for SageMaker)
Alternative Options and Their Drawbacks:
Option B:Creating a single IAM group and adding data scientists to it does not
directly associate the group with notebook instances. IAM groups are used to
manage user permissions, not to assign roles to AWS resources like notebook
instances.
Option C:Using a single IAM user with the AdministratorAccess policy is not
recommended due to security risks associated with granting broad permissions
and the challenges in managing shared user credentials.
Option D:Associating an IAM group with a role and then with notebook instances is
not a valid approach, as IAM groups cannot be directly associated with AWS
resources.
Conclusion:Option A is the most effective solution to centralize and manage permissions
for SageMaker notebook instances, aligning with AWS best practices for IAM role
management.
A company is planning to use Amazon Redshift ML in its primary AWS account. The
source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3
bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?
A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.
Explanation:
S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a
public IPv4 address, ensuring that data transfer between the primary and secondary
accounts is secure and private.
Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow
access from the primary account's IAM principals to provide the necessary permissions.
Interface VPC Endpoints: Required for private communication between the VPC and
Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without
public internet access.
This configuration meets the requirement to avoid public IPv4 addresses and allows secure
and private communication between the accounts.
A financial company receives a high volume of real-time market data streams from an
external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data
points.
Which solution will meet these requirements with the LEAST operational overhead?
A. Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
B. Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
C. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
D. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.
Explanation:
This solution is the most efficient and involves the least operational overhead:
Amazon Kinesis data streams efficiently handle real-time ingestion of high-volume
streaming data.
Amazon Managed Service for Apache Flink provides a fully managed environment for
stream processing with built-in support for RANDOM_CUT_FOREST, an algorithm
designed for anomaly detection in real-time streaming data.
This approach eliminates the need for deploying and managing additional infrastructure like
SageMaker endpoints, Lambda functions, or external tools, making it the most scalable and
operationally simple solution.
A company has implemented a data ingestion pipeline for sales transactions from its
ecommerce website. The company uses Amazon Data Firehose to ingest data into
Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60
seconds. An OpenSearch linear model generates real-time sales forecasts based on the
data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency
for the real-time dashboard.
Which change to the architecture will meet these requirements?
A. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting
thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as
possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further
optimize the data ingestion for sub-second latency. This approach minimizes latency while
maintaining the operational simplicity of using Firehose.
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