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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.
An ML engineer has trained a neural network by using stochastic gradient descent (SGD).
The neural network performs poorly on the test set. The values for training loss and
validation loss remain high and show an oscillating pattern. The values decrease for a few
epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?
A. Introduce early stopping.
B. Increase the size of the test set.
C. Increase the learning rate.
D. Decrease the learning rate.
Explanation:
In training neural networks using Stochastic Gradient Descent (SGD), the learning rate is a
critical hyperparameter that influences the convergence behavior of the model. Observing
oscillations in training and validation loss suggests that the learning rate may be too high,
causing the optimization process to overshoot minima in the loss landscape.
Understanding the Impact of Learning Rate:
High Learning Rate:A high learning rate can cause the model parameters to
update too aggressively, leading to oscillations or divergence in the loss function.
This manifests as the loss decreasing for a few epochs and then increasing,
repeating this cycle without stable convergence.
Low Learning Rate:A low learning rate results in smaller parameter updates,
allowing the model to converge more steadily to a minimum, albeit potentially at a
slower pace.
Recommended Action:
Decreasing the learning rate allows for more precise adjustments to the model parameters,
facilitating smoother convergence and reducing oscillations in the loss function. This
adjustment helps the model settle into minima more effectively, improving overall
performance.
Supporting Evidence:
Research indicates that large learning rates can lead to phenomena such as "catapults,"
where spikes in training loss occur due to aggressive updates. Reducing the learning rate
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in
batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?
A. Run the primary node, core nodes, and task nodes on On-Demand Instances.
B. Run the primary node, core nodes, and task nodes on Spot Instances.
C. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the
cluster, including data storage (HDFS) and processing. Running them on On-Demand
Instances ensures high availability and prevents data loss, as Spot Instances can be
interrupted. The task nodes, which handle additionalprocessing but do not store data, can
use Spot Instances to reduce costs without compromising the cluster's resilience or data
integrity. This configuration balances cost-effectiveness and reliability.
A company wants to predict the success of advertising campaigns by considering the color
scheme of each advertisement. An ML engineer is preparing data for a neural network
model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?
A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
B. Implement padding to ensure that all color feature vectors have the same length.
C. Perform dimensionality reduction on the color categories.
D. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
Explanation: One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.
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