Amazon AWS Certified Machine Learning - Specialty Exam Dumps & Practice Test Questions

Question 1:

Why is this model considered suitable for deployment, based on its evaluation results?

A. The model’s accuracy is 86%, and the cost of false negatives is lower than that of false positives.
B. The model’s precision is 86%, which is lower than its accuracy.
C. The model’s accuracy is 86%, and the cost of false positives is lower than the cost of false negatives.
D. The model’s precision is 86%, which is higher than its accuracy.

Answer: C

Explanation:

To assess why this model is fit for production, it's important to understand the meaning of the performance metrics and their business impact.

Accuracy measures how often the model’s predictions (both positives and negatives) are correct out of all predictions made. An accuracy of 86% suggests that 86% of the time, the model correctly predicts outcomes, which may sound good at first glance. However, accuracy alone is not sufficient to determine if a model is viable for deployment, especially when costs related to prediction errors vary.

Precision represents how many of the predicted positives are actually true positives. For instance, in a customer churn prediction model, precision tells us how often customers predicted to leave actually do leave.

In the context of a mobile network company predicting customer churn, two types of errors occur:

• False positives: Customers wrongly predicted to churn, who receive retention incentives unnecessarily.

• False negatives: Customers predicted not to churn but who actually do, resulting in lost customers and revenue.

From a cost perspective, false negatives are generally more expensive because they mean missed opportunities to retain customers. False positives, while they incur some cost due to unnecessary incentives, are usually less costly overall.

Option C correctly states that the model has an 86% accuracy and importantly recognizes that the cost of false positives is less than the cost of false negatives. This cost dynamic makes the model practical for production since it prioritizes avoiding costly false negatives even if it means tolerating some false positives.

Options A, B, and D either misunderstand the relationship between precision and accuracy or do not consider the cost implications crucial for real-world deployment decisions.

In conclusion, the model is suitable because it strikes a balance where the less expensive mistake (false positives) is tolerated more than the costlier one (false negatives), making C the correct choice.

Question 2:

Which approach should the Machine Learning Specialist take to predict products that users would like by analyzing their similarity to other users?

A. Develop a content-based filtering recommendation system using Apache Spark ML on Amazon EMR.
B. Develop a collaborative filtering recommendation system using Apache Spark ML on Amazon EMR.
C. Develop a model-based filtering recommendation system using Apache Spark ML on Amazon EMR.
D. Develop a combinative filtering recommendation system using Apache Spark ML on Amazon EMR.

Answer: B

Explanation:

The objective here is to predict products a user might like based on how similar their preferences are to those of other users. This aligns directly with the concept of collaborative filtering in recommendation systems.

Collaborative filtering works by identifying patterns or similarities between users or items based on historical behavior such as ratings, purchases, or clicks. If users A and B have shown similar tastes or behavior in the past, products liked by user A can be recommended to user B. There are two main flavors: user-based collaborative filtering (finding similar users) and item-based collaborative filtering (finding similar items). Since the question highlights user similarity, user-based collaborative filtering is the ideal choice.

Option A, content-based filtering, uses product attributes (such as genre, description, or features) to recommend items similar to what a user has already engaged with. While useful, it does not rely on similarities between users, so it does not fit the requirement here.

Option C, model-based filtering, is a broader term that often refers to more sophisticated machine learning approaches like matrix factorization or latent factor models. These can be part of collaborative filtering but generally require more complex modeling. While potentially powerful, the basic requirement is to leverage user similarity, making standard collaborative filtering a better initial fit.

Option D, “combinative filtering,” is not a standard term in recommendation systems. While hybrid recommendation systems exist, combining multiple approaches, this option lacks precise definition and doesn’t directly apply.

Therefore, the best approach to meet the objective of recommending products based on user similarity is to build a collaborative filtering recommendation engine using Apache Spark ML on Amazon EMR, making B the correct answer.

Question 3:

Which approach requires the least complexity and effort to convert .CSV data into Apache Parquet format before saving it in Amazon S3?

A. Using Apache Kafka Streams on Amazon EC2 to ingest .CSV data and Kafka Connect S3 to serialize it as Parquet
B. Using Amazon Kinesis Data Streams to ingest .CSV data and Amazon Glue to transform it into Parquet
C. Using Apache Spark Structured Streaming on an Amazon EMR cluster to ingest and convert .CSV data into Parquet
D. Using Amazon Kinesis Data Streams combined with Amazon Kinesis Data Firehose to convert .CSV data into Parquet

Correct answer: B

Explanation:

When deciding how to transform .CSV data into Apache Parquet format for storage on Amazon S3, the key is to choose a method that minimizes implementation complexity while maintaining efficiency. Among the options, Option B stands out as the easiest to deploy due to Amazon Glue’s fully managed ETL capabilities.

Option A involves running Apache Kafka Streams on EC2 instances, which requires setting up and managing Kafka infrastructure manually. While Kafka Connect simplifies data integration, managing the underlying EC2 environment and Kafka cluster adds significant operational overhead, making this option more complex and time-consuming.

Option B leverages Amazon Kinesis Data Streams for real-time ingestion and Amazon Glue for ETL processing. Glue’s serverless architecture automatically handles scaling, schema inference, and transformation logic. It supports CSV-to-Parquet conversion natively and integrates seamlessly with S3 and Kinesis. This reduces the need for manual infrastructure management, making it the simplest and most streamlined choice.

Option C utilizes Apache Spark on Amazon EMR, which provides powerful data processing but requires provisioning and managing a cluster. Spark demands more expertise in cluster configuration and tuning, increasing setup time and maintenance effort, which makes it less ideal when minimizing implementation complexity is the priority.

Option D uses Amazon Kinesis Data Firehose, a managed service that can convert data formats automatically, including Parquet. Although easy to configure, Firehose is better suited for straightforward transformations and may lack the advanced control and flexibility Glue offers for ETL jobs. For more complex data pipelines, Glue is often preferred.

In summary, Option B provides the most effortless and scalable approach by combining Amazon Kinesis Data Streams with the managed ETL capabilities of Amazon Glue, enabling quick, low-maintenance conversion of CSV data to Parquet format with minimal infrastructure overhead.

Question 4:

Which machine learning model is most suitable in Amazon SageMaker for forecasting air quality (in parts per million) for the next two days?

A. k-Nearest Neighbors (kNN) algorithm set as a regressor
B. Random Cut Forest (RCF) algorithm
C. Linear Learner algorithm set as a regressor
D. Linear Learner algorithm set as a classifier

Correct answer: C

Explanation:

The goal is to forecast air quality measurements, a continuous variable, over the next two days based on a year of daily historical data. This problem is a time series forecasting task that requires predicting numerical values. Let’s evaluate the model options accordingly.

Option A, k-Nearest Neighbors (kNN) used as a regressor, while capable of regression, is generally not the best for time series forecasting. kNN relies on similarity measures but does not inherently model temporal dependencies or trends. This makes it less effective for predicting future values in sequential data where temporal patterns are crucial.

Option B, Random Cut Forest (RCF), is specifically designed for anomaly detection rather than forecasting. It excels at identifying outliers in data streams but does not provide a mechanism to predict continuous future values. Therefore, RCF is unsuitable for predicting air quality levels.

Option C, Linear Learner set as a regressor, is well-suited for this task. It performs regression by fitting a linear model to input features and target values. When historical time series data is properly structured, the Linear Learner can model trends and relationships to forecast continuous variables like air quality. This algorithm is efficient, scalable, and designed to handle regression problems in SageMaker, making it ideal for this scenario.

Option D, Linear Learner as a classifier, is inappropriate because classification predicts discrete categories rather than continuous numerical values. Since air quality measurements are continuous, using a classifier will not provide meaningful forecasts.

In conclusion, Option C is the best choice. The Linear Learner regressor can effectively model the relationship between past air quality data and future values, enabling accurate predictions for the upcoming two days, leveraging the full year of historical input.

Question 5:

What is the best approach for a Data Engineer to keep customer credit card data encrypted and secure while building a model using a dataset that contains sensitive credit card information?

A. Implement a custom encryption method and store the data on an Amazon SageMaker instance inside a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
B. Apply an IAM policy to encrypt data stored in an Amazon S3 bucket and use Amazon Kinesis to discard real credit card numbers and replace them with fake ones.
C. Use an Amazon SageMaker launch configuration to encrypt the data when it is copied to the SageMaker instance in a VPC and apply the SageMaker Principal Component Analysis (PCA) algorithm to shorten credit card numbers.
D. Utilize AWS Key Management Service (KMS) to encrypt data on both Amazon S3 and SageMaker, and redact the credit card numbers from the dataset using AWS Glue.

Correct answer: D

Explanation:

Protecting sensitive customer credit card information during data processing requires robust encryption and careful handling of sensitive fields to ensure security and regulatory compliance. The most secure and industry-standard method is to encrypt the data both at rest and in transit and to mask or redact credit card numbers when they are unnecessary for modeling.

Option A suggests using a custom encryption algorithm and randomizing credit card numbers with the SageMaker DeepAR algorithm. Custom encryption generally lacks the rigor and trust of standardized solutions like AWS KMS and can introduce vulnerabilities. Randomizing credit card numbers distorts the data, potentially reducing model accuracy and violating compliance standards because the underlying data changes arbitrarily.

Option B involves relying on IAM policies for encryption and using Amazon Kinesis to discard and replace real credit card numbers with fake ones. While IAM policies manage access, they do not perform encryption themselves. Also, substituting fake credit card numbers risks compromising the integrity and usefulness of the dataset, leading to flawed model training.

Option C talks about encrypting data once it is copied to SageMaker and applying PCA to reduce credit card number lengths. PCA is a dimensionality reduction technique that does not ensure data security or mask sensitive fields. Reducing the length of credit card numbers is neither a security measure nor compliant with data protection standards.

Option D offers the best solution by leveraging AWS KMS, which provides strong, managed encryption for data at rest and in transit in both Amazon S3 and SageMaker environments. Additionally, using AWS Glue to redact or mask credit card numbers ensures sensitive data is either removed or anonymized while retaining data utility for analysis. This method adheres to best practices in data security and compliance frameworks such as PCI DSS.

Therefore, option D is the most secure, compliant, and practical approach for handling sensitive credit card data during model development.

Question 6:

Why can the Machine Learning Specialist not see the SageMaker notebook instance or its related resources inside the VPC?

A. SageMaker notebook instances run on EC2 instances in the customer account but operate outside of VPCs by default.
B. SageMaker notebook instances are based on Amazon ECS services running in the customer’s account.
C. SageMaker notebook instances run on EC2 instances managed by AWS service accounts.
D. SageMaker notebook instances are hosted on AWS ECS instances within AWS service accounts.

Correct answer: A

Explanation:

The core reason the Machine Learning Specialist cannot see the SageMaker notebook instance or its EBS volumes within the customer’s Virtual Private Cloud (VPC) lies in how Amazon SageMaker manages notebook instances behind the scenes. Although SageMaker notebook instances are indeed based on Amazon EC2 instances, these EC2 instances are not launched directly within the customer’s VPC by default.

Instead, SageMaker manages these EC2 instances on behalf of the customer using AWS-managed infrastructure. The notebook instances run outside the customer’s VPC and are isolated from the customer’s direct network resources. Because of this design, the EC2 instances backing the notebook are not visible in the customer’s AWS Management Console under EC2 or VPC resources.

This abstraction allows SageMaker to handle infrastructure scaling, patching, and maintenance seamlessly, reducing operational overhead for users. However, it means that the typical AWS controls and visibility into EC2 instances or associated EBS volumes are not available for these notebook instances in the customer’s account.

When it comes to storage, SageMaker abstracts the management of EBS volumes for notebooks, which are used to persist data. While customers can interact with the notebook and its filesystem, the underlying EBS volumes and EC2 instances are managed entirely by SageMaker and don’t appear in the customer’s VPC or EC2 dashboards.

Thus, option A correctly explains why the SageMaker notebook instances are not visible inside the VPC—they are EC2 instances managed by AWS outside of the customer’s VPC by default, ensuring simplified management but limiting direct visibility.

Question 7:

Which method enables the Specialist to monitor latency, CPU usage, and memory consumption in real-time during a load test on a SageMaker model endpoint?

A Analyze SageMaker logs saved to Amazon S3 using Amazon Athena and Amazon QuickSight for real-time visualization.
B Create an Amazon CloudWatch dashboard to consolidate and display latency, CPU, and memory metrics emitted by SageMaker.
C Develop custom CloudWatch Logs, then utilize Amazon Elasticsearch Service (Amazon ES) and Kibana to query and visualize SageMaker log data.
D Forward CloudWatch Logs generated by SageMaker to Amazon ES and employ Kibana for log analysis and visualization.

Answer: B

Explanation:

In this scenario, the goal is to effectively observe performance metrics such as latency, CPU, and memory utilization while conducting a load test on an Amazon SageMaker endpoint. The most suitable solution is to leverage Amazon CloudWatch to collect and visualize these metrics in real time.

Amazon CloudWatch is a fully managed monitoring service designed to provide metrics and logs for AWS resources and applications. For SageMaker endpoints, CloudWatch automatically gathers important operational data like CPU utilization, memory usage, and request latency. This native integration enables immediate insight into system behavior without additional overhead.

Creating a CloudWatch dashboard (Option B) is an efficient way to visualize multiple relevant metrics in one unified interface. This centralized view allows the Specialist to continuously monitor resource utilization and latency during the load test, making it easier to detect bottlenecks or performance degradation. It also aids in adjusting auto-scaling policies dynamically based on the observed metrics.

Option A, which suggests analyzing logs stored in S3 via Athena and QuickSight, is better suited for retrospective analysis rather than real-time monitoring. This method introduces delays because logs need to be ingested, queried, and visualized separately.

Options C and D involve using Amazon Elasticsearch Service and Kibana to analyze logs. While these tools are powerful for deep log analysis and searching, setting up Elasticsearch clusters and Kibana dashboards introduces complexity and maintenance overhead. They are not as straightforward as CloudWatch for real-time metric monitoring, especially when SageMaker already exports performance data to CloudWatch.

In conclusion, Option B is the most straightforward and operationally efficient method for real-time performance monitoring during load tests on SageMaker endpoints.

Question 8:

Which solution requires the least amount of setup to run SQL queries on both structured and unstructured data stored in an Amazon S3 bucket?

A Use AWS Data Pipeline for data transformation and Amazon RDS for running SQL queries.
B Utilize AWS Glue for data cataloging and Amazon Athena to execute SQL queries directly on the data.
C Employ AWS Batch to perform ETL operations and use Amazon Aurora for querying the transformed data.
D Apply AWS Lambda for data transformation and Amazon Kinesis Data Analytics to query the data.

Answer: B

Explanation:

The requirement is to query both structured and unstructured datasets residing in an Amazon S3 bucket using SQL, while minimizing operational complexity and setup time.

Option B, using AWS Glue and Amazon Athena, is the best fit. AWS Glue is a serverless data catalog and ETL service that can automatically discover schemas and organize metadata for data stored in S3. It simplifies managing data structure, making it easier to query the data. Amazon Athena is a serverless interactive query service that allows running SQL queries directly on S3 data without needing to move or transform it into a database. This integration makes querying fast, flexible, and maintenance-free since it eliminates the need to manage any infrastructure.

Option A involves using AWS Data Pipeline and Amazon RDS. Data Pipeline is useful for orchestrating ETL workflows but requires managing complex pipelines and data movement. RDS requires provisioning and managing relational databases, schema design, and scaling, adding considerable overhead compared to serverless options.

Option C relies on AWS Batch and Amazon Aurora. AWS Batch is designed for batch computing jobs, not interactive queries, and Aurora requires database management. This approach involves multiple services, increased complexity, and longer deployment times.

Option D uses AWS Lambda for transformation and Kinesis Data Analytics for querying. Lambda is event-driven and not optimal for large-scale data transformation on S3, while Kinesis Data Analytics is intended for streaming data, not static datasets in S3. This solution would be unnecessarily complicated for the task.

Therefore, Option B is the most efficient and low-maintenance solution. The combination of AWS Glue’s cataloging and Athena’s serverless querying provides a straightforward way to query S3 data using SQL with minimal effort.

Question 9:

What is the best method for the Specialist to utilize the entire dataset to train a machine learning model when the data is too large to fit in the SageMaker notebook’s local storage?

A. Load a small portion of the data into the SageMaker notebook and verify that the training code and model parameters are working correctly. Then, run a SageMaker training job on the complete dataset stored in the S3 bucket using Pipe input mode.
B. Start an Amazon EC2 instance with an AWS Deep Learning AMI, attach the S3 bucket, and train on a small data sample. Afterward, return to SageMaker to train using the full dataset.
C. Use AWS Glue to train on a small subset of data to verify compatibility with SageMaker, then start a SageMaker training job on the full dataset in S3 using Pipe input mode.
D. Load a small subset of data into the SageMaker notebook for local training and code validation, then use an EC2 instance with AWS Deep Learning AMI attached to S3 for training on the entire dataset.

Correct answer: A

Explanation:

The main challenge here is the large dataset size, which cannot be fully loaded into the SageMaker notebook instance’s limited local storage (5 GB EBS volume). Option A is the optimal approach because it follows a two-step process that balances efficiency and scalability. First, the Specialist tests the training code and model behavior on a smaller subset of the data within the SageMaker notebook. This step ensures that the code runs correctly and the model parameters are reasonable without consuming excessive resources. After confirming this, the actual training uses SageMaker’s Pipe input mode, which streams data directly from the S3 bucket to the training instance. This method avoids loading the entire dataset into local storage, making it possible to handle very large datasets effectively.

Option B introduces an EC2 instance, which adds unnecessary complexity. Although it can technically work, the question’s context emphasizes using SageMaker. Switching between EC2 and SageMaker is less streamlined and does not leverage SageMaker’s efficient data streaming capabilities.

Option C involves AWS Glue, a service intended for ETL (extract, transform, load) tasks rather than model training, so it’s not suited for this purpose.

Option D combines both SageMaker and EC2, complicating the workflow without delivering clear benefits. Using Pipe input mode in SageMaker alone is simpler and more effective.

In summary, A maximizes efficiency by testing locally with a subset and then streaming the full dataset directly from S3 using SageMaker’s built-in mechanisms, making it the best practice for training on large datasets.

Question 10:

What is the recommended process for the Specialist to prepare the data stored in an SQL database for training a machine learning model in SageMaker?

A. Create a direct connection from the SageMaker notebook to the SQL database and query the data as needed.
B. Use AWS Data Pipeline to transfer data from the Microsoft SQL Server database to Amazon S3, and access the data via the S3 location in the notebook.
C. Move the data into Amazon DynamoDB and connect to DynamoDB from the notebook to retrieve data for training.
D. Migrate the data to Amazon ElastiCache with AWS DMS and connect the notebook to ElastiCache for fast data access during training.

Correct answer: B

Explanation:

The Specialist is tasked with training a model using data stored in a Microsoft SQL Server database within Amazon RDS, aiming for a scalable and efficient solution. Option B is the best practice for handling this scenario because it decouples data storage from the training process by transferring data into Amazon S3, which is well suited for large datasets. AWS Data Pipeline automates the extraction and transfer process, ensuring data consistency and availability in S3. SageMaker integrates seamlessly with S3, allowing the training job to efficiently access data using input modes optimized for large datasets. This approach avoids bottlenecks that can occur when querying the database directly during training and aligns with AWS architecture best practices for machine learning workflows.

Option A suggests querying the SQL database directly from the notebook. While this might work for small datasets or initial prototyping, it is not scalable. Direct database queries during training risk performance degradation, network latency issues, and potential contention on the database, making it inefficient for large-scale model training.

Option C proposes moving the data into DynamoDB, a NoSQL key-value store optimized for low-latency read/write workloads but not suited for large volumes of structured training data typically used in ML. This option would complicate data handling without improving training efficiency.

Option D suggests migrating data to ElastiCache, a caching service designed for fast in-memory access in applications rather than storage of large historical datasets. Using ElastiCache for ML training data is inappropriate because it does not provide persistent, scalable storage.

In conclusion, option B offers a scalable, reliable, and efficient pipeline by transferring SQL database data into Amazon S3 using AWS Data Pipeline. This solution supports large datasets and optimizes SageMaker training performance, making it the best practice for the Specialist.