Amazon AWS Certified Machine Learning - Specialty Exam Dumps & Practice Test Questions
Question No 1:
A cloud-based monitoring system gathers and stores a vast amount of scale metrics data, reaching 1 terabyte every minute. This data is saved in Amazon S3, and the Research and Analytics Team frequently runs complex analytical queries on it using Amazon Athena. However, as the dataset expands rapidly, query performance has deteriorated significantly. The research team wants a storage format that will optimize query speed, reduce the volume of data scanned, and speed up analytical operations in Athena.
Which storage format should be used for storing the data in Amazon S3 to achieve the best query performance in Amazon Athena?
A. CSV files
B. Parquet files
C. Compressed JSON
D. RecordIO
Correct Answer: B. Parquet files
Explanation:
Amazon Athena lets users query data directly stored in Amazon S3 with SQL. Although Athena supports multiple data formats, query efficiency varies widely depending on the chosen format.
When dealing with very large datasets—such as 1 TB per minute—minimizing the data scanned in each query is crucial. Columnar storage formats like Apache Parquet are well-suited for this purpose. Parquet organizes data by column instead of by row, making it highly efficient for analytics focused on selected columns.
Unlike CSV or JSON, which are row-based and require scanning entire rows even when only some columns are needed, Parquet allows Athena to scan only the relevant columns. This greatly reduces scanned data size, improves query speed, and lowers costs. Parquet also supports advanced compression and encoding, which further reduces storage and accelerates processing.
Compressed JSON can reduce file size but remains row-based and is not optimized for Athena queries. RecordIO is commonly used in machine learning contexts like SageMaker but is not ideal for Athena analytical workloads.
Therefore, to enhance query speed and efficiency on a rapidly growing dataset, the data should be stored in Parquet format in Amazon S3.
Question No 2:
A gaming company launched a new online game that is free to play but includes premium features requiring payment. To improve marketing and revenue, the company wants to predict which new users are likely to become paying customers within their first year.
They have data from 1 million users:
1,000 users became paying customers (positive class).
999,000 users did not pay (negative class).
Each user’s dataset contains 200 features such as age, device type, location, and gameplay behavior. The data science team trained a random forest classifier on this dataset. While the model achieved over 99% accuracy on training data, its predictions on the test set were unreliable.
Given this situation, which two actions should the team take to improve model performance?
A. Increase the number of trees in the random forest to capture more complex patterns.
B. Combine the test dataset into the training set to increase training data size.
C. Create synthetic positive samples by duplicating and slightly modifying existing ones.
D. Adjust the loss function to impose a higher penalty on false negatives.
E. Adjust the loss function to impose a higher penalty on false positives.
Correct Answers:
C. Create synthetic positive samples by duplicating and slightly modifying existing ones.
D. Adjust the loss function to impose a higher penalty on false negatives.
Explanation:
This scenario presents a highly imbalanced classification problem, where the positive class (paying users) is only 0.1% of the dataset. Such imbalance often causes models like random forests to favor the majority class, resulting in high training accuracy but poor generalization on unseen data.
Option C addresses the imbalance by generating more positive samples through data augmentation — duplicating and slightly altering positive instances. This technique, similar to SMOTE, helps the model better recognize minority class characteristics and improve prediction accuracy on positive cases.
Option D modifies the model’s cost function to penalize false negatives more heavily. In business terms, missing potential paying users (false negatives) is more costly than mistakenly labeling non-payers as payers (false positives). By increasing the penalty for false negatives, the model becomes more sensitive to detecting true paying users, aligning with business goals.
Option A (adding more trees) does not address the class imbalance or poor generalization.
Option B (mixing test and training data) causes data leakage, invalidating the evaluation process.
Option E (penalizing false positives more) could lead to missing paying users, which is counterproductive.
Therefore, generating synthetic positive samples and adjusting the loss function to reduce false negatives are the most effective steps to improve model performance in this context.
Question No 3:
A Data Scientist is developing a regression machine learning model to predict continuous future patient outcomes. The dataset contains clinical and demographic information from 4,000 patients over 65 years old, all diagnosed with a certain age-related degenerative disease. The aim is to forecast health progression based on patient characteristics and treatment history.
Initial models have shown poor results. Upon closer examination, the Data Scientist finds that 450 records have the age recorded as 0, which is clearly incorrect since all patients should be 65 or older. All other features in those 450 records appear consistent with the rest of the data.
What is the best way to handle these incorrect age values before retraining the model?
A. Remove all records where age is 0 from the dataset.
B. Replace the age values of 0 with the mean or median age from the dataset.
C. Eliminate the age feature entirely and train the model using the remaining features.
D. Apply k-means clustering to address the missing or incorrect features.
Correct answer: B. Replace the age values of 0 with the mean or median age from the dataset.
Explanation:
In machine learning, especially with healthcare data, maintaining data quality is essential for good model performance and interpretability. Since age is a critical predictor in this context — given the disease worsens with age — it should not be discarded.
The problem stems from invalid age entries recorded as 0, likely due to data entry errors or missing values defaulting to 0. Removing those 450 records (Option A) would discard over 11% of the dataset, which could negatively affect model generalization.
Dropping the age feature (Option C) would remove a valuable predictor and reduce the model’s ability to capture important age-related trends. Using k-means clustering (Option D) is not suitable here, as it is an unsupervised method for grouping data, not for imputing single numerical values, and would add unnecessary complexity.
The best approach is to impute the erroneous age values using the mean or median from the valid data. This preserves all records and corrects the data error. Median imputation is often preferred in healthcare datasets for robustness against outliers.
By cleaning the data this way, the model can better learn accurate patterns and provide more reliable predictions.
Question No 4:
A Data Science team needs to build a scalable, cost-effective storage solution for a growing volume of machine learning training datasets. These datasets are generated frequently, possibly multiple times per day, and must be easily accessible for SQL queries. The solution should automatically scale with storage needs, minimize costs, and support SQL-based data exploration.
Which storage option best fits these requirements?
A. Store datasets as files in Amazon S3
B. Store datasets as files on an Amazon EBS volume attached to an EC2 instance
C. Store datasets as tables in a multi-node Amazon Redshift cluster
D. Store datasets as global tables in Amazon DynamoDB
Correct answer: A. Store datasets as files in Amazon S3
Explanation:
For storing large, frequently updated machine learning datasets with SQL query capability, Amazon S3 combined with tools like Amazon Athena or Redshift Spectrum is the ideal choice.
Amazon S3 offers automatic, virtually unlimited scalability without manual management, and you pay only for the storage used. It supports multiple storage classes (Standard, Infrequent Access, Glacier) to optimize cost depending on access patterns. Data can be stored in various formats like CSV, JSON, or Parquet.
Amazon Athena is a serverless service that allows direct SQL queries against data stored in S3 without needing infrastructure provisioning, enabling flexible and cost-efficient data exploration.
Other options are less suitable:
B (EBS + EC2) requires manual scaling and is costlier.
C (Redshift) supports SQL but maintaining a multi-node cluster is expensive and less flexible for frequently changing datasets.
D (DynamoDB) is a NoSQL database and does not support SQL queries on large datasets efficiently.
Therefore, storing datasets as files in Amazon S3 is the best option for scalable, cost-effective storage with SQL query support.
Question No 5:
A Machine Learning Specialist deployed a recommendation model on an e-commerce website to suggest products based on users' browsing and purchase history. Initially, the model worked well, increasing customer engagement and average purchases per session. However, in recent months, the quality of recommendations has declined. Users are not interacting with the recommendations, and purchase patterns have returned to what they were before the model was introduced.
The Specialist confirms that the model has not been changed or retrained since it was launched over a year ago. The company’s product catalog has also changed significantly, with many new items added and older ones removed. The Specialist believes the decline in model performance might be related to these changes but is unsure how to proceed.
What is the best action the Specialist should take to improve and restore the model’s performance?
A. Rebuild the entire model architecture from scratch to handle a dynamic inventory.
B. Regularly update the model’s hyperparameters to prevent drift.
C. Retrain the model from scratch using only the original dataset and add a regularization term to account for inventory changes.
D. Periodically retrain the model using both the original training data and new data that reflects the updated product inventory.
Correct Answer:
D. Periodically retrain the model using both the original training data and new data reflecting changes in product inventory.
Explanation:
Machine learning models, especially recommendation systems, are sensitive to changes in input data distribution. The model’s declining performance is likely due to concept drift, where the underlying data patterns shift over time, such as changes in product availability or customer behavior.
The model was initially trained on associations between users and products available at that time. When the inventory changes significantly, those learned associations become outdated, causing irrelevant or missing recommendations.
The best practice is to retrain the model periodically by combining historical data with recent data that captures current inventory and user interactions. This approach allows the model to retain long-term trends while adapting to new information.
Option A is unnecessary unless the model design itself is flawed. Option B only adjusts hyperparameters, which won’t resolve data drift. Option C retrains on old data alone, missing important recent patterns.
Regular retraining on both historical and recent data keeps the model relevant, robust, and effective in maintaining engagement and purchase growth.
Question No 6:
A Machine Learning Specialist at a fashion retail company is designing a data ingestion system for a new Amazon S3-based data lake. The system must scale well, support multiple types of analytics, and integrate with machine learning workflows in the future.
The Specialist plans to address these use cases:
Real-time analytics to track customer behavior and inventory status
Interactive analytics on historical data for business reporting
Clickstream analytics to analyze user navigation on the website
Product recommendation engine using both historical and behavioral data
Which combination of AWS services should the Specialist choose to build a scalable and future-ready data ingestion architecture?
A.
Use AWS Glue as the data catalog
Use Amazon Kinesis Data Streams and Kinesis Data Analytics for real-time insights
Use Amazon Kinesis Data Firehose to send clickstream data to Amazon Elasticsearch Service (Amazon ES)
Use Amazon EMR to build personalized product recommendations
B.
Use Amazon Athena as the data catalog
Use Kinesis Data Streams and Kinesis Data Analytics for near-real-time analytics
Use Kinesis Firehose for clickstream data
Use AWS Glue for generating recommendations
C.
Use AWS Glue as the data catalog
Use Kinesis Data Streams and Kinesis Data Analytics for historical insights
Use Kinesis Firehose to deliver data to Amazon ES
Use Amazon EMR for recommendations
D.
Use Amazon Athena as the data catalog
Use Kinesis Data Streams and Kinesis Data Analytics for historical insights
Use DynamoDB Streams for clickstream analytics
Use AWS Glue for recommendations
Correct Answer:
A. Use AWS Glue as the data catalog; Kinesis Data Streams and Kinesis Data Analytics for real-time data; Kinesis Data Firehose delivering clickstream data to Amazon ES; and Amazon EMR for recommendation engine processing.
Explanation:
Option A offers a well-rounded and scalable architecture addressing all required use cases:
AWS Glue provides a centralized metadata catalog that enables schema management and data discovery, integrating seamlessly with other AWS analytics tools.
Amazon Kinesis Data Streams captures streaming data such as website clicks, while Kinesis Data Analytics enables near-real-time SQL queries on streaming data, perfect for live insights on customer behavior and inventory.
Kinesis Data Firehose efficiently transports streaming clickstream data to Amazon Elasticsearch Service, allowing fast search and analysis of user interactions via Kibana dashboards.
Amazon EMR supports large-scale data processing with frameworks like Apache Spark, suitable for running machine learning algorithms to generate personalized product recommendations.
The other options are less suitable:
Option B misuses Athena as a data catalog, though Athena is designed for querying data rather than cataloging it.
Options C and D assign roles to services that do not align well with their primary functions, such as using Kinesis for historical data or DynamoDB Streams for clickstream analytics.
Thus, Option A best aligns with the needs for real-time data ingestion, scalable analytics, and machine learning integration.
Question No 7:
A company is facing unsatisfactory results when using the default built-in image classification algorithm in Amazon SageMaker. After reviewing the situation, the Data Science team decides to move from the default ResNet-based model to an Inception neural network architecture to enhance accuracy.
What are the best ways to implement this change using Amazon SageMaker? (Select two correct options.)
A. Change the built-in SageMaker image classification algorithm to use the Inception architecture and continue with model training.
B. Reach out to AWS support to request that the default image classification algorithm be replaced with Inception.
C. Create a custom Docker container that uses a TensorFlow Estimator with an Inception model, then use this container for training in SageMaker.
D. Utilize a TensorFlow Estimator in SageMaker and write custom code to load and train an Inception model.
E. Manually download the Inception network on an EC2 instance and connect it to a SageMaker Jupyter notebook for training.
Correct answers:
C. Create a custom Docker container that uses a TensorFlow Estimator with an Inception model, then use this container for training in SageMaker.
D. Utilize a TensorFlow Estimator in SageMaker and write custom code to load and train an Inception model.
Explanation:
Amazon SageMaker’s built-in image classification algorithms are generally based on ResNet architectures and are designed for common use cases. However, these built-in algorithms cannot be customized to use alternative deep learning architectures like Inception. Therefore, if the company wants to switch from ResNet to Inception, more flexible methods must be adopted.
Option A is incorrect because SageMaker’s built-in algorithms do not allow customization at the architectural level; they are closed systems focused on ease of use rather than configurability. Option B is not correct since AWS support does not modify the fundamental behavior of built-in algorithms for individual users.
The proper approach is to use either pre-built frameworks or custom containers that provide more control. Option C is valid because SageMaker supports using custom containers. By packaging a Docker image containing TensorFlow and the custom Inception model, the company gains full control over the training code and dependencies.
Option D is also valid and generally simpler if the developer is comfortable coding. SageMaker allows using framework estimators like TensorFlow, where custom scripts can load and train the Inception model architecture on the dataset.
Option E is not recommended because it involves training outside of SageMaker’s managed training environment, which undermines the scalability and manageability benefits SageMaker offers. Installing the model manually in a notebook instance is neither efficient nor suitable for production workloads.
Hence, options C and D are the correct and practical solutions to implement the Inception model within Amazon SageMaker.
Question No 8:
A Machine Learning Specialist has developed a deep learning image classification model. During evaluation, the model achieves very high accuracy (99%) on the training set but performs poorly on the testing set with only 75% accuracy. This indicates an overfitting problem, where the model memorizes the training data instead of learning generalizable patterns.
Which action should the Specialist take to reduce overfitting, and why?
A. Increase the learning rate to help the optimizer avoid local minima.
B. Increase the dropout rate at the flatten layer to encourage generalization.
C. Increase the number of neurons in the dense layer to make the model more complex.
D. Increase the number of training epochs to help the model find a better solution.
Correct answer: B. Increase the dropout rate at the flatten layer to encourage generalization.
Explanation:
Overfitting happens when a model performs exceptionally well on training data but poorly on unseen data. This means the model has learned specific details and noise from the training set that do not apply to new inputs, resulting in poor generalization.
In this case, the large gap between training accuracy (99%) and testing accuracy (75%) signals overfitting.
One effective way to combat overfitting in neural networks is to use dropout. Dropout is a regularization technique that randomly disables a fraction of neurons during training, forcing the model to learn more robust and generalized features instead of relying too heavily on particular neurons or pathways. Increasing the dropout rate at the flatten or dense layers reduces overfitting by encouraging the network to avoid dependency on specific nodes.
Reviewing the other options:
Option A: Raising the learning rate can cause unstable training and might worsen performance.
Option C: Adding more neurons increases model complexity, which usually intensifies overfitting rather than solving it.
Option D: Extending the number of epochs may further overfit the model if no regularization is applied, as it continues to memorize the training data.
Therefore, option B is the best method to reduce overfitting and improve the model’s ability to generalize.
Question No 9:
What is the best AWS service to use when you need to build, train, and deploy machine learning models without managing infrastructure?
A AWS Lambda
B Amazon SageMaker
C Amazon EC2
D AWS Glue
Correct Answer: B
Explanation:
This question explores which AWS service simplifies the machine learning lifecycle by removing the need to handle underlying infrastructure. The correct service should enable building, training, and deploying models seamlessly.
Option A, AWS Lambda, is a serverless compute service designed for event-driven code execution. It does not specialize in machine learning model lifecycle management.
Option B is Amazon SageMaker, the comprehensive machine learning service from AWS that provides integrated tools for data preparation, model building, training, tuning, and deployment. SageMaker handles infrastructure provisioning and scalability, which greatly reduces the operational burden on developers and data scientists.
Option C, Amazon EC2, offers raw compute resources requiring users to configure and manage all aspects of the machine learning environment, including software installation and scaling.
Option D, AWS Glue, is primarily an ETL (extract, transform, load) service used for data cataloging and preparation, but not for building or deploying machine learning models.
Amazon SageMaker is the ideal solution because it accelerates the end-to-end machine learning process by providing managed Jupyter notebooks, built-in algorithms, automatic model tuning, and one-click deployment. This allows organizations to focus more on the data science aspects rather than on infrastructure, making model iteration and deployment more efficient and reliable.
Question No 10:
Which technique should be employed to prevent overfitting when training a deep learning model using Amazon SageMaker?
A Increase the learning rate during training
B Use dropout layers and early stopping strategies
C Reduce the amount of training data
D Train the model for more epochs
Correct Answer: B
Explanation:
This question examines strategies to avoid overfitting—a situation where a model learns the training data too well but performs poorly on unseen data.
Option A, increasing the learning rate, often makes the training process unstable and can prevent the model from converging properly. This is not an effective way to combat overfitting.
Option B is the right choice. Incorporating dropout layers in neural networks randomly disables a fraction of neurons during training, which helps the model generalize better. Early stopping halts training once the model’s performance on a validation set stops improving, preventing over-training.
Option C, reducing training data, usually worsens overfitting because the model has less variety to learn from, making it more likely to memorize training samples.
Option D, training for more epochs, often leads to more overfitting since the model continues to adapt to noise in the training set.
To address overfitting, best practices include using dropout, early stopping, regularization techniques, and expanding training datasets. Amazon SageMaker provides built-in support for these techniques, making it easier for developers to build robust models with better generalization to new data.
