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Question 1:
A sports betting application relies on machine learning to predict match outcomes. These predictions are obtained by invoking an Amazon SageMaker endpoint. A machine learning engineer must ensure the SageMaker model stays available, even with a surge of traffic expected during an upcoming finals game.
Which solution would offer the highest scalability to meet this requirement?
A.Configure auto-scaling on the SageMaker endpoint.
B. Increase the SageMaker’s instance size to accommodate heavy traffic.
C. Host the SageMaker model in an AWS Inferentia-based instance.
D. Use Amazon SageMaker Neo to optimize the model for inference.
Answer: A
Explanation:
When you host a model on SageMaker, you can optionally to configure autoscaling. This feature dynamically adjusts the number of instances in response to actual workload changes, giving you a more effective way to handle varying levels of traffic. When the workload increases, autoscaling brings more instances online. When the workload decreases, autoscaling removes unnecessary instances, helping you reduce your compute cost.
Hence, the correct answer is: Configure auto-scaling on the SageMaker endpoint.
The option that says: Increase the SageMaker’s instance size to accommodate heavy traffic is incorrect. While using a larger instance size would indeed allow for handling more traffic, there is a limit to this approach. Past a certain point, the instance will inevitably become overloaded again.
The option that says: Use Amazon SageMaker Neo to optimize the model for inference is incorrect because SageMaker Neo is just a service used to optimize an ML-model for inference on cloud instances and edge devices.
The option that says: Host the SageMaker model in an AWS Inferentia-based instance is incorrect. AWS Inferentia is simply an AWS-owned chip purposely designed for accelerating inference workloads at a reduced cost. While AWS Inferentia-based instances can offer improved inference performance, they are not immune to failures due to a surge in load. A better approach is to increase capacity thru auto-scaling.
References:
https://aws.amazon.com/blogs/machine-learning/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker/
https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html
Check out this Amazon SageMaker Cheat Sheet:
https://tutorialsdojo.com/amazon-sagemaker/
Question 2:
A Machine Learning Specialist is building a model that can identify whether a spike in highway traffic indicates an event requiring a timely response such as vehicle collisions and pedestrian accidents. The Specialist will use a time-series dataset for training and the severity of the event will be based on the anomaly score given by the model.
Which machine learning algorithm is the MOST appropriate for this task?
A. Multiclass classification
B. Random Cut Forest
C. Linear Regression
D. K-means algorithm
Answer: B
Explanation:
Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set. These are observations that diverge from otherwise well-structured or patterned data. Anomalies can manifest as unexpected spikes in time series data, breaks in periodicity, or unclassifiable data points. They are easy to describe in that when viewed in a plot, they are often easily distinguishable from the “regular“ data. Including these anomalies in a data set can drastically increase the complexity of a machine learning task since the “regular“ data can often be described with a simple model.
With each data point, RCF associates an anomaly score. Low score values indicate that the data point is considered “normal.“ High values indicate the presence of an anomaly in the data. The definitions of “low“ and “high“ depend on the application but common practice suggests that scores beyond three standard deviations from the mean score are considered anomalous.
Hence, the correct answer is: Random Cut Forest.
Linear Regression is incorrect. Although you can spot outliers by determining how far the distance of data from the regression line is, this algorithm is not the most suitable for this type of problem.
K-means algorithm is incorrect. This is an unsupervised algorithm mainly used for finding discrete groupings within data, where members of a group are as similar as possible to one another and as different as possible from members of other groups. It would be difficult to implement an anomaly detection with K-means especially with a time-series dataset as you don‘t know the k-parameter to choose. Take note that this algorithm uses a fixed number of clusters defined by the k parameter. Since we‘re dealing with data that changes over time, it would be tricky to adjust the correct k-parameters needed.
Multiclass classification is incorrect because this is mainly used for labeling target data into a specific category. This means that the predicted results are bound within a fixed number of classifications that were defined during training. In this scenario, we‘re dealing with continuous data — the anomaly score can take any values and it‘s not bound by a fixed range.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html
https://aws.amazon.com/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random-cut-forest-algorithm-for-anomaly-detection/
Question 3:
A water utility provider wants to forecast water consumption in a city to meet the demand. Based on past data, water demand follows seasonal patterns, is affected by local weather conditions, and is linked to the usage of other utilities in the area.
Which solution requires the LEAST development effort to meet their requirements?
A. Use AWS Glue DataBrew to preprocess the training data with weather information. Train the model using the TensorFlow Time Series (TF-TS) library.
B. Train the model using Amazon Forecast’s Exponential Smoothing (ETS) algorithm and use the built-in Weather Index featurization.
C. Train the model using Amazon Forecast’s DeepAR+ algorithm and use the built-in Weather Index featurization.
D. Train the model using Amazon SageMaker DeepAR forecasting algorithm
Answer: C
Explanation:
Amazon Forecasts DeepAR+ is a supervised learning algorithm that utilizes recurrent neural networks (RNNs) to forecast scalar time series data. Unlike traditional methods such as ARIMA or ETS, DeepAR+ trains a single model that considers all the time series together. This approach is particularly useful when dealing with datasets containing multiple similar time series across different units.
By using DeepAR+ and incorporating the Weather Index featurization, the model can take into account the impact of weather conditions on water consumption without gathering weather information manually. The Weather Index provides a way to include external factors in the forecasting model, allowing it to capture the relationship between water demand and weather patterns.
Hence, the correct answer is: Train the model using Amazon Forecast’s DeepAR+ algorithm and use the built-in Weather Index featurization.
The option that says: Train the model using Amazon SageMaker DeepAR forecasting algorithm is incorrect because this requires more development effort (e.g., configuring the SageMaker environment, creating an instance, configuring training jobs) compared to using the built-in capabilities of Amazon Forecast.
The option that says: Use AWS Glue DataBrew to preprocess the training data with weather information. Train the model using the TensorFlow Time Series (TF-TS) library is incorrect. This option involves manual preprocessing of the data and then implementing the forecasting model using TF-TS library.
The option that says: Train the model using Amazon Forecast’s Exponential Smoothing (ETS) algorithm and use the built-in Weather Index featurization is incorrect. While ETS is a simple and effective forecasting algorithm, it may not capture complex patterns in the water consumption data as well as the DeepAR+ algorithm.
References:
https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-choosing-recipes.html
https://aws.amazon.com/blogs/machine-learning/start-your-successful-journey-with-time-series-forecasting-with-amazon-forecast/
Question 4:
A Machine Learning Specialist is preparing the dataset to be used for training a linear learner model in Amazon SageMaker. During exploratory data analysis, he has detected multiple feature columns that have missing values. The percentage of missing data across the whole training dataset is about 10%. The Specialist is worried that this might cause bias to his model that can lead to inaccurate results.
Which approach will MOST likely yield the best result in reducing the bias caused by missing values?
A. Use supervised learning methods to estimate the missing values for each feature.
B. Compute the mean of non-missing values in the same column and use the result to replace missing values.
C. Drop the columns that include missing values because they only account for 10% of the training data.
D. Compute the mean of non-missing values in the same row and use the result to replace missing values.
Answer: A
Explanation:
After getting to know your data through data summaries and visualizations, you might want to transform your variables further to make them more meaningful. This is known as feature processing.
One of the common feature processing is imputing missing values to replace missing values with the mean or median value. It is important to understand your data before choosing a strategy for replacing missing values.
While the abovementioned strategy is possible, using a supervised learning method to approximate missing values will most likely provide better results. Supervised learning applied to the imputation of missing values is an active field of research.
Hence, the correct answer is: Use supervised learning methods to estimate the missing values for each feature.
The option that says: Drop the columns that include missing values because they only account for 10% of the training data is incorrect. Doing this will remove features that might be valuable to your machine learning task. Hence, this is not the most effective method.
The following options are both valid imputation techniques but it‘s not likely for them to give better estimates than a supervised learning method.
– Compute the mean of non-missing values in the same row and use the result to replace missing values.
– Compute the mean of non-missing values in the same column and use the result to replace missing values.
References:
https://docs.aws.amazon.com/machine-learning/latest/dg/feature-processing.html
https://www.hilarispublisher.com/open-access/a-comparison-of-six-methods-for-missing-data-imputation-2155-6180-1000224.pdf
Tutorials Dojo‘s AWS Certified Machine Learning Exam Study Guide:
Question 5:
A marketing analyst wants to group current and prospective customers into 10 groups based on their attributes. The analyst wants to send mailings to prospective customers in the group which has the highest percentage of current customers.
As an AWS ML Specialist, which SageMaker algorithm would you recommend to meet this requirement?
A. Latent Dirichlet Allocation
B. Principal Component Analysis
C. K-means
D. KNN
Answer: C
Explanation:
K-means is an unsupervised learning algorithm. It attempts to find discrete groupings within data, where members of a group are as similar as possible to one another and as different as possible from members of other groups. You define the attributes that you want the algorithm to use to determine similarity.
Amazon SageMaker uses a modified version of the web-scale k-means clustering algorithm. Compared with the original version of the algorithm, the version used by Amazon SageMaker is more accurate. Like the original algorithm, it scales to massive datasets and delivers improvements in training time. To do this, the version used by Amazon SageMaker streams mini-batches (small, random subsets) of the training data.
K-means is the right algorithm to uncover discrete groupings within the given dataset.
Incorrect options:
KNN – Amazon SageMaker k-nearest neighbors (k-NN) algorithm is an index-based algorithm. It uses a non-parametric method for classification or regression. For classification problems, the algorithm queries the k points that are closest to the sample point and returns the most frequently used label of their class as the predicted label. For regression problems, the algorithm queries the k closest points to the sample point and returns the average of their feature values as the predicted value.
As there is no historic data with labels, so KNN is ruled out.
Principal Component Analysis – Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
PCA is used for dimensionality reduction, so it is not the right fit for the given use case.
Latent Dirichlet Allocation – The Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is an unsupervised learning algorithm that attempts to describe a set of observations as a mixture of distinct categories. LDA is most commonly used to discover a user-specified number of topics shared by documents within a text corpus. Here each observation is a document, the features are the presence (or occurrence count) of each word, and the categories are the topics. Since the method is unsupervised, the topics are not specified up front, and are not guaranteed to align with how a human may naturally categorize documents. The topics are learned as a probability distribution over the words that occur in each document. Each document, in turn, is described as a mixture of topics.
LDA is used for topic modeling, so it is not the right fit for the given use case.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html
For a full set of 550 questions. Go to
https://skillcertpro.com/product/aws-certified-machine-learning-specialty-practice-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.
Question 6:
An online real estate database company provides information on the housing prices for all states in the US by capturing information such as house size, age, location etc. The company is capturing data for a city where the typical housing prices are around $200K except for some houses that are more than 100 years old with an asking price of about $1 million. These heritage houses will never be listed on the platform.
Which of the following data processing steps would you recommend to address this use case?
A. One-hot encode the data for all houses in this city and then train the model
B. Drop the heritage houses from the training data and then train the model
C. Normalize the data for all houses in this city and then train the model
D. Standardize the data for all houses in this city and then train the model
Answer: B
Explanation:
Data preparation (also referred to as “data preprocessing”) is the process of transforming raw data to make it ready for downstream ML algorithms. As the heritage houses are clear outliers in terms of price and will never be listed, it is best to drop these from the training data and then train the model.
Incorrect options:
Normalize the data for all houses in this city and then train the model
Standardize the data for all houses in this city and then train the model
Normalization typically means rescaling the values into a range of [0,1].
Standardization typically means rescaling values to have a mean of 0 and a standard deviation of 1.
While normalizing and standardizing is a valid strategy but for this use-case it would end up injecting noise into the model due to the data from the heritage houses. So both these options are incorrect.
One-hot encode the data for all houses in this city and then train the model – One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms for processing. One-hot encoding is used only for nominal (without any ordering of values, which are also known as ordinals) categorical features. The housing data such as house size, house age should not be one-hot encoded. So this option is incorrect.
Question 7:
A leading technology company offers a fast-track leadership program to the best performing executives at the company. At any point in time, more than a thousand executives are part of this leadership program.
Which of the following is the best visualization type to analyze the salary distribution for these executives?
A. Histogram
B. Bar Chart
C. Bubble Chart
D. Pie Chart
Answer: A
Explanation:
Histogram is best suited to analyze the underlying distribution of data for the given use-case.
Highly recommend this resource for a deep-dive on visualizations in ML:
https://medium.com/data-science-bootcamp/data-visualization-in-machine-learning-beyond-the-basics-baf2cbea8989
Incorrect options:
Pie Chart
Bar Chart
Bubble Chart
These three chart types are not the right fit for analyzing the underlying distribution of data.
Reference:
https://chartio.com/learn/charts/essential-chart-types-for-data-visualization/
Question 8:
Which of the following are the mandatory hyperparameters for the SageMaker K-means algorithm? (Select two)
A. epochs
B. k
C. mini_batch_size
D. feature_dim
Answer: B and D
Explanation:
✅ k
Represents the number of clusters to form.
Mandatory for SageMaker K-means.
It tells the algorithm how many centroids to initialize and iterate around.
✅ feature_dim
Specifies the number of features in the input dataset.
Mandatory as the algorithm needs to understand the dimensionality of the input vectors.
❌ epochs
This is not a hyperparameter for the K-means algorithm in SageMaker.
It is more relevant in supervised learning algorithms (e.g., linear learner, deep learning models).
❌ mini_batch_size
This is an optional hyperparameter in SageMaker K-means.
It helps with efficiency and performance but is not required to be set explicitly.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/k-means-api-config.html
Question 9:
When you create a training job with the SageMaker API, Amazon SageMaker replicates the entire dataset from S3 to ML compute instances by default.
What of the following options would you use to replicate only a subset of the data on each ML compute instance?
A. Set the S3DataDistributionType field to FullyReplicated
B. Set the S3DataDistributionType field to ShardedByS3Key
C. Set the S3DataType field to ShardedByS3Key
D. Set the S3Uri field to ShardedByS3Key
Answer: B
Explanation:
Set the S3DataDistributionType field to ShardedByS3Key
S3DataDistributionType is a field used to describe the S3DataSource for SageMaker.
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key for S3DataDistributionType field.
Incorrect options:
Set the S3DataType field to ShardedByS3Key – S3DataType is a field used to describe the S3DataSource for SageMaker. This option has been added as a distractor since the value ShardedByS3Key is associated with the S3DataDistributionType field.
Set the S3Uri field to ShardedByS3Key – S3Uri is a field used to describe the S3DataSource for SageMaker. This option has been added as a distractor since the value ShardedByS3Key is associated with the S3DataDistributionType field.
Set the S3DataDistributionType field to FullyReplicated – If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_S3DataSource.html
Question 10:
A data science team is trying to port a legacy binary classification model to Amazon SageMaker. In the legacy workflow, the data engineering component was handled using Apache Spark and scikit-learn based preprocessors.
Which feature of Amazon SageMaker can be utilized for seamless integration of the legacy functionality into the new SageMaker model?
A. Automatic Scaling
B. Inference Pipeline
C. Elastic Inference
D. Batch Transform
Answer: B
Explanation:
Correct: B. Inference Pipeline
Inference Pipeline is the feature in Amazon SageMaker designed to handle sequential data processing and model invocation during real-time inference.
It allows you to chain together multiple containers—each running a different processing step or a model—into a single SageMaker endpoint.
The preprocessors (developed with Apache Spark or scikit-learn) can be containerized and run in one or more initial steps of the pipeline, with the final classification model running in the last container.
This provides a seamless integration because the data is automatically passed from the output of the preprocessor container(s) to the input of the model container, mimicking the legacy workflow where the preprocessors and model were run sequentially.
This approach is ideal for porting existing preprocessing logic that needs to be executed right before the model's prediction.
Incorrect:
Incorrect: A. Automatic Scaling
Automatic Scaling is a feature that adjusts the number of instances supporting a SageMaker endpoint or a training job in response to traffic or workload demands.
While important for handling production load, it does not address the problem of integrating the legacy preprocessing logic with the model execution flow.
It is about capacity management, not data processing/model execution architecture.
Incorrect: C. Elastic Inference
Elastic Inference (EI) allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and SageMaker instances.
Its primary goal is to reduce the cost of deep learning inference by providing just the right amount of GPU acceleration, without needing to use full, expensive GPU instances.
This feature is focused on improving performance/cost efficiency for the model execution itself and does not provide a mechanism for chaining preprocessors (like Spark/scikit-learn) with the final model.
Incorrect: D. Batch Transform
Batch Transform is used for making predictions on a large dataset asynchronously (in batches), rather than through a persistent, real-time endpoint.
It can certainly be used to run a model and preprocessors (it can process data using a transform job), but it is a batch processing feature, not an architecture for a seamless real-time endpoint integration.
While it can incorporate preprocessors, the term "seamless integration...into the new SageMaker model" generally refers to deploying a comprehensive, unified real-time inference unit, which is the domain of the Inference Pipeline. For an endpoint, Batch Transform is not the correct solution.
For a full set of 550 questions. Go to
https://skillcertpro.com/product/aws-certified-machine-learning-specialty-practice-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.