Question # 1
| While working on a neural network project, a Machine Learning Specialist discovers thai
some features in the data have very high magnitude resulting in this data being weighted
more in the cost function What should the Specialist do to ensure better convergence
during backpropagation? | | A. Dimensionality reduction
| | B. Data normalization
| | C. Model regulanzation
| | D. Data augmentation for the minority class |
B. Data normalization
Explanation: Data normalization is a data preprocessing technique that scales the
features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of
features with high magnitude on the cost function and improves the convergence during
backpropagation. Data normalization can be done using different methods, such as minmax
scaling, z-score standardization, or unit vector normalization. Data normalization is
different from dimensionality reduction, which reduces the number of features; model
regularization, which adds a penalty term to the cost function to prevent overfitting; and
data augmentation, which increases the amount of data by creating synthetic samples.
Question # 2
A company is building a new version of a recommendation engine. Machine learning (ML)
specialists need to keep adding new data from users to improve personalized
recommendations. The ML specialists gather data from the users’ interactions on the
platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this
data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored
in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish,
with each script taking at least an hour. The company wants to move the scripts out of
Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort? | | A. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL.
Store the results in Amazon S3.
| | B. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda
function. Execute the pipeline by triggering Lambda executions. Store the results in
Amazon S3.
| | C. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store
the results in Amazon S3.
| | D. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a
step function by using the AWS Step Functions Data Science SDK. Store the results in
Amazon S3. |
C. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store
the results in Amazon S3.
Explanation: The best approach to address all of the requirements with the least
development effort is to create an AWS Glue job, convert the scripts to PySpark, execute
the pipeline, and store the results in Amazon S3. This is because:
-
AWS Glue is a fully managed extract, transform, and load (ETL) service that
makes it easy to prepare and load data for analytics 1. AWS Glue can run Python
and Scala scripts to process data from various sources, such as Amazon S3,
Amazon DynamoDB, Amazon Redshift, and more 2. AWS Glue also provides a
serverless Apache Spark environment to run ETL jobs, eliminating the need to
provision and manage servers 3.
-
PySpark is the Python API for Apache Spark, a unified analytics engine for largescale
data processing 4. PySpark can perform various data transformations and
manipulations on structured and unstructured data, such as cleaning, enriching,
and compressing 5. PySpark can also leverage the distributed computing power of
Spark to handle terabytes of data efficiently and scalably 6.
-
By creating an AWS Glue job and converting the scripts to PySpark, the company
can move the scripts out of Amazon EC2 into a more managed solution that will
eliminate the need to maintain servers. The company can also reduce the
development effort by using the AWS Glue console, AWS SDK, or AWS CLI to
create and run the job 7. Moreover, the company can use the AWS Glue Data
Catalog to store and manage the metadata of the data sources and targets 8.
The other options are not as suitable as option C for the following reasons:
-
Option A is not optimal because loading the data into an Amazon Redshift cluster
and executing the pipeline by using SQL will incur additional costs and complexity
for the company. Amazon Redshift is a fully managed data warehouse service that
enables fast and scalable analysis of structured data . However, it is not designed
for ETL purposes, such as cleaning, transforming, enriching, and compressing
data. Moreover, using SQL to perform these tasks may not be as expressive and
flexible as using Python scripts. Furthermore, the company will have to provision
and configure the Amazon Redshift cluster, and load and unload the data from
Amazon S3, which will increase the development effort and time.
-
Option B is not feasible because loading the data into Amazon DynamoDB and
converting the scripts to an AWS Lambda function will not work for the company’s
use case. Amazon DynamoDB is a fully managed key-value and document
database service that provides fast and consistent performance at any scale .
However, it is not suitable for storing and processing terabytes of data daily, as it
has limits on the size and throughput of each table and item . Moreover, using
AWS Lambda to execute the pipeline will not be efficient or cost-effective, as
Lambda has limits on the memory, CPU, and execution time of each function .
Therefore, using Amazon DynamoDB and AWS Lambda will not meet the
company’s requirements for processing large amounts of data quickly and reliably.
-
Option D is not relevant because creating a set of individual AWS Lambda
functions to execute each of the scripts and building a step function by using the
AWS Step Functions Data Science SDK will not address the main issue of moving
the scripts out of Amazon EC2. AWS Step Functions is a fully managed service
that lets you coordinate multiple AWS services into serverless workflows . The
AWS Step Functions Data Science SDK is an open source library that allows data
scientists to easily create workflows that process and publish machine learning
models using Amazon SageMaker and AWS Step Functions . However, these
services and tools are not designed for ETL purposes, such as cleaning,
transforming, enriching, and compressing data. Moreover, as mentioned in option
B, using AWS Lambda to execute the scripts will not be efficient or cost-effective
for the company’s use case.
Question # 3
A retail company uses a machine learning (ML) model for daily sales forecasting. The
company’s brand manager reports that the model has provided inaccurate results for the
past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the
forecasting with the actual daily sales data and the predictions of the model. The AWS
Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon
SageMaker Studio notebook to gain an understanding about the source of the model's
inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's
degradation MOST accurately? | | A. Create a histogram of the daily sales over the last 3 weeks. In addition, create a
histogram of the daily sales from before that period.
| | B. Create a histogram of the model errors over the last 3 weeks. In addition, create a
histogram of the model errors from before that period.
| | C. Create a line chart with the weekly mean absolute error (MAE) of the model.
| | D. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition,
create a scatter plot of daily sales versus model error from before that period. |
B. Create a histogram of the model errors over the last 3 weeks. In addition, create a
histogram of the model errors from before that period.
Explanation: The best way to visualize the model’s degradation is to create a histogram of
the model errors over the last 3 weeks and compare it with a histogram of the model errors
from before that period. A histogram is a graphical representation of the distribution of
numerical data. It shows how often each value or range of values occurs in the data. A
model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of
the model errors, the ML team can see if there is a significant change in the shape, spread,
or center of the distribution. This can indicate if the model is underfitting, overfitting, or
drifting from the data. A line chart or a scatter plot would not be as effective as a histogram
for this purpose, because they do not show the distribution of the errors. A line chart would
only show the trend of the errors over time, which may not capture the variability or outliers.
A scatter plot would only show the relationship between the errors and another variable,
such as daily sales, which may not be relevant or informative for the model’s performance.
Question # 4
| A Machine Learning Specialist is preparing data for training on Amazon SageMaker The
Specialist is transformed into a numpy .array, which appears to be negatively affecting the
speed of the training
What should the Specialist do to optimize the data for training on SageMaker'? | | A. Use the SageMaker batch transform feature to transform the training data into a
DataFrame
| | B. Use AWS Glue to compress the data into the Apache Parquet format
| | C. Transform the dataset into the Recordio protobuf format
| | D. Use the SageMaker hyperparameter optimization feature to automatically optimize the
data |
C. Transform the dataset into the Recordio protobuf format
Explanation: The Recordio protobuf format is a binary data format that is optimized for
training on SageMaker. It allows faster data loading and lower memory usage compared to
other formats such as CSV or numpy arrays. The Recordio protobuf format also supports
features such as sparse input, variable-length input, and label embedding. To use the
Recordio protobuf format, the data needs to be serialized and deserialized using the
appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio
protobuf format as a content type for training and inference.
Question # 5
A company plans to build a custom natural language processing (NLP) model to classify
and prioritize user feedback. The company hosts the data and all machine learning (ML)
infrastructure in the AWS Cloud. The ML team works from the company's office, which has
an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport
attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The
company does not allow the ML team to access Amazon SageMaker notebooks through
connections that use the public internet. The connection must stay within a private network
and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort? | | A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the
notebook through a VPN connection and the VPC endpoint.
| | B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to
the bastion host through a VPN connection. Access the SageMaker notebook from the
bastion host.
| | C. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a
NAT gateway. Log in to the bastion host through a VPN connection. Access the
SageMaker notebook from the bastion host.
| | D. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint
through a VPN connection and the NAT gateway. |
A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the
notebook through a VPN connection and the VPC endpoint.
Explanation: In this scenario, the company requires that access to the Amazon
SageMaker notebook remain within the AWS internal network, avoiding the public internet.
By creating a VPC interface endpoint for SageMaker, the company can ensure that traffic
to the SageMaker notebook remains internal to the VPC and is accessible over a private
connection. The VPC interface endpoint allows private network access to AWS services,
and it operates over AWS’s internal network, respecting the security and connectivity
policies the company requires.
This solution requires minimal development effort compared to options involving bastion
hosts or NAT gateways, as it directly provides private network access to the SageMaker
notebook.
Question # 6
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an
XGBoost model, which is working appropriately for test data. However, with unknown data,
it is not working as expected. The existing parameters are provided as follows.
Which parameter tuning guidelines should the Specialist follow to avoid overfitting? | | A. Increase the max_depth parameter value. | | B. Lower the max_depth parameter value.
| | C. Update the objective to binary:logistic.
| | D. Lower the min_child_weight parameter value. |
B. Lower the max_depth parameter value.
Explanation: Overfitting occurs when a model performs well on the training data but poorly
on the test data. This is often because the model has learned the training data too well and
is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist
should lower the max_depth parameter value. This will reduce the complexity of the model
and make it less likely to overfit. According to the XGBoost documentation1, the
max_depth parameter controls the maximum depth of a tree and lower values can help
prevent overfitting. The documentation also suggests other ways to control overfitting, such
as adding randomness, using regularization, and using early stopping1.
Question # 7
A Marketing Manager at a pet insurance company plans to launch a targeted marketing
campaign on social media to acquire new customers Currently, the company has the
following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential
new customers on social media? | | A. Use regression on customer profile data to understand key characteristics of consumer
segments Find similar profiles on social media.
| | B. Use clustering on customer profile data to understand key characteristics of consumer
segments Find similar profiles on social media.
| | C. Use a recommendation engine on customer profile data to understand key
characteristics of consumer segments. Find similar profiles on social media
| | D. Use a decision tree classifier engine on customer profile data to understand key
characteristics of consumer segments. Find similar profiles on social media |
B. Use clustering on customer profile data to understand key characteristics of consumer
segments Find similar profiles on social media.
Explanation: Clustering is a machine learning technique that can group data points into
clusters based on their similarity or proximity. Clustering can help discover the underlying
structure and patterns in the data, as well as identify outliers or anomalies. Clustering can
also be used for customer segmentation, which is the process of dividing customers into
groups based on their characteristics, behaviors, preferences, or needs. Customer
segmentation can help understand the key features and needs of different customer
segments, as well as design and implement targeted marketing campaigns for each
segment. In this case, the Marketing Manager at a pet insurance company plans to launch
a targeted marketing campaign on social media to acquire new customers. To do this, the
Manager can use clustering on customer profile data to understand the key characteristics
of consumer segments, such as their demographics, pet types, policy preferences,
premiums paid, claims made, etc. The Manager can then find similar profiles on social
media, such as Facebook, Twitter, Instagram, etc., by using the cluster features as filters or
keywords. The Manager can then target these potential new customers with personalized
and relevant ads or offers that match their segment’s needs and interests. This way, the
Manager can implement a machine learning model to identify potential new customers on
social media.
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