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

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

Question No 1:

Based on the model evaluation results, why is this a viable model for production?

A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
B. The precision of the model is 86%, which is less than the accuracy of the model.
C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
D. The precision of the model is 86%, which is greater than the accuracy of the model.

Answer: C

Explanation:

To determine why this model is viable for production, let's understand what each term and evaluation result implies:

Accuracy refers to the proportion of correct predictions (both true positives and true negatives) out of the total number of predictions. An accuracy of 86% means that 86% of all predictions made by the model are correct, which seems like a good overall performance.
Precision is the proportion of true positive predictions (correctly predicted churned customers) out of all predicted positive cases (all customers predicted to churn). High precision means that when the model predicts a customer will churn, it is usually correct.

In the context of the mobile network company, false positives (predicting a customer will churn when they actually won't) would incur costs due to offering incentives unnecessarily, while false negatives (failing to predict a customer will churn when they actually do) would result in losing customers without offering any retention incentives, which is a more expensive outcome.

Option A: The model being 86% accurate does not necessarily make it viable if the cost of false negatives is greater than false positives. However, it is the comparison of costs that makes a model viable. If the cost of false positives is less than that of false negatives, offering incentives to some customers who might not churn (false positives) is a better strategy than failing to identify those who actually will churn (false negatives).
Option C: This option correctly indicates that the model's accuracy is 86%, but more importantly, it correctly identifies the cost dynamics. Since false positives (incorrectly identifying churners) incur less cost compared to false negatives (failing to identify churners), the model is viable for production. In this scenario, offering incentives to a few false positives is less costly than not identifying customers who are about to churn.
Option B: This statement is incorrect because precision is not directly compared to accuracy in terms of a "less than" relationship. The precision is a metric of its own, and accuracy is just one part of the evaluation.
Option D: While it's true that precision and accuracy are different metrics, saying that precision is greater than accuracy doesn't provide a meaningful argument for why the model is viable for production. What's more important is how precision and the model's cost dynamics influence decision-making, not just the comparison between these two metrics.

In summary, the model is viable for production because the company can afford the cost of false positives (incorrect churn predictions) more easily than the cost of false negatives, which would result in losing customers without offering them retention incentives. Therefore, option C is the correct answer.

Question No 2:

What should the Machine Learning Specialist do to meet the objective of predicting which products users would like based on their similarity to other users?

A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR
C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR
D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR

Answer: B

Explanation:

In this scenario, the Machine Learning Specialist's goal is to use users' behavior and product preferences to predict which products users would like based on their similarity to other users. This objective aligns most closely with collaborative filtering. Collaborative filtering is a technique used in recommendation systems where recommendations are made based on the similarity between users or items. This method doesn't require explicit knowledge of item content; rather, it uses the idea that users who agreed on past interactions or preferences are likely to agree in the future.

The two main types of collaborative filtering are:

User-based collaborative filtering, which makes predictions based on the similarity between users.
Item-based collaborative filtering, which makes predictions based on the similarity between items.

Given the focus on users' similarity to other users in this scenario, user-based collaborative filtering is the most appropriate approach. It involves finding other users who have similar product preferences and recommending products those similar users have liked or interacted with.

Option A, content-based filtering, would focus on recommending items that are similar to those a user has interacted with before, based on item attributes (e.g., category, description, tags). While content-based filtering can be effective for personalized recommendations, it does not directly focus on user similarity, which is the central point of this scenario.

Option C, model-based filtering, refers to using machine learning models to predict ratings or preferences, which is a more advanced version of collaborative filtering, typically using matrix factorization or similar techniques. While this could also be a potential approach, the core idea here is to use similarity between users, which is better addressed by standard collaborative filtering techniques.

Option D, combinative filtering, is not a widely recognized standard term in the field of recommendation systems. While hybrid methods that combine multiple approaches exist (e.g., combining content-based and collaborative filtering), this term does not directly apply to the scenario described.

Therefore, the most suitable approach is B, building a collaborative filtering recommendation engine. This would enable the system to predict which products users would like based on their similarity to other users.

Question No 3:

Which solution takes the least effort to implement for transforming .CSV data to Apache Parquet format before storing it on Amazon S3?

A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet
C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet
D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet

Answer: B

Explanation:

To analyze and optimize operations efficiently while converting data to the Apache Parquet format, it’s crucial to balance ease of implementation with system performance. The four options presented each have their strengths, but B represents the solution that requires the least effort to implement based on the specific requirements.

Option A (Apache Kafka Streams on EC2 with Kafka Connect): Kafka is a powerful tool for handling high-throughput streaming data. However, deploying and managing Apache Kafka on EC2 instances requires a significant setup effort, including configuring Kafka, managing EC2 instances, and ensuring scalability. While Kafka Connect can help with the data transformation and integration, it adds additional operational overhead that is unnecessary for a straightforward solution like this. Additionally, managing an entire Kafka infrastructure just for the data transformation step adds unnecessary complexity.
Option B (Amazon Kinesis Data Streams and Amazon Glue): This solution involves using Amazon Kinesis Data Streams to ingest the data and Amazon Glue to convert the .CSV data to Parquet format. Amazon Glue is a fully managed ETL (Extract, Transform, Load) service that integrates seamlessly with other AWS services, especially S3 and Kinesis. Glue can easily read CSV files and transform them into Parquet with minimal setup. This is a fully managed service, so the overhead of managing infrastructure is greatly reduced, making it a very efficient solution in terms of implementation effort.
Option C (Apache Spark Structured Streaming on Amazon EMR): Apache Spark is another powerful tool for stream processing and transforming data, but it requires more setup and management, especially when using it in an Amazon EMR cluster. While Spark excels in performance and flexibility, it requires additional expertise to configure, manage, and optimize Spark clusters. Given that the goal is to minimize effort, this approach is more complex compared to the fully managed Amazon Glue service.
Option D (Amazon Kinesis Data Streams with Kinesis Data Firehose): Kinesis Data Firehose is a fully managed service that can automatically ingest data and convert it to various formats, including Parquet. It simplifies the process compared to running your own infrastructure, as it is managed end-to-end by AWS. However, Amazon Glue (in option B) is better suited for complex transformations and has more flexibility and control over the data conversion process. Kinesis Data Firehose is ideal for simple, straightforward transformations but might not be as flexible as Glue for certain use cases.

In summary, Option B provides the easiest and most efficient way to handle real-time data ingestion and conversion to the Parquet format with minimal setup and management effort. By using Amazon Kinesis Data Streams in combination with Amazon Glue, the team can focus on the data transformations without worrying about managing infrastructure or complex configurations.

Question No 4:

Which model is MOST likely to provide the best results in Amazon SageMaker for forecasting air quality in parts per million for the next 2 days?

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

Answer: C

Explanation:

In this scenario, the city is looking to forecast air quality based on a year's worth of daily data, which indicates a time series forecasting problem. Let’s break down the given options and their suitability for this type of task:

Option A: k-Nearest-Neighbors (kNN) algorithm with a predictor_type of regressor
While kNN can be used for regression tasks, it is generally not the best choice for time series forecasting, especially when forecasting future values from sequential data. kNN is typically better suited for classification problems or when there is less dependence on the ordering of data. In time series forecasting, the dependency on historical data and the sequential nature of the data are important, which makes kNN less effective for this purpose.
Option B: Random Cut Forest (RCF) algorithm
RCF is an anomaly detection algorithm rather than a forecasting algorithm. While it can identify outliers or anomalous patterns in data, it is not designed specifically for predicting future values in time series data. Since the objective here is to predict air quality for the next two days, RCF would not be suitable for the task at hand.
Option C: Linear Learner algorithm with a predictor_type of regressor
Linear Learner is a versatile machine learning algorithm for regression tasks, which makes it an excellent choice for time series forecasting when the relationship between input features and the target variable can be modeled linearly. In this case, forecasting air quality levels is a regression task, and Linear Learner is designed to predict continuous values, such as the air quality levels in parts per million. It is especially effective when the data shows a linear trend or when time series data can be transformed into a format suitable for regression. Since the data is daily and covers a full year, the model can learn from the historical trends to make future predictions.
Option D: Linear Learner algorithm with a predictor_type of classifier
This option is not appropriate because classification is used when predicting discrete categories or classes, not continuous values like air quality levels. Since air quality is a continuous variable (expressed in parts per million), using classifier as the predictor type is incorrect for this regression task.

Conclusion: The best choice for forecasting air quality levels based on the available historical data is Option C, as Linear Learner is designed to handle regression problems, and time series forecasting fits well within this paradigm when using a suitable regressor. This model will help predict continuous values (air quality) for future days effectively, given the historical data.

Question No 5:

How can a Data Engineer ensure the data remains encrypted and the credit card information is secure while building a model using a dataset containing customer credit card information?

A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Correct answer: D

Explanation:

To ensure the security of customer credit card information, the most effective approach involves encrypting sensitive data both at rest and in transit and properly masking or redacting credit card numbers when they are not necessary for model training or analysis.

Option A suggests using a custom encryption algorithm and randomizing credit card numbers with the SageMaker DeepAR algorithm. While custom encryption may provide security, it is not as robust and reliable as using industry-standard encryption methods like AWS KMS. Additionally, randomizing credit card numbers is not a good practice because it could alter critical data, making the model less accurate and potentially leading to compliance issues.
Option B proposes using IAM policies for encryption and using Amazon Kinesis to discard credit card numbers and replace them with fake ones. This approach is flawed because using fake data could introduce inaccuracies into the model. Moreover, it lacks focus on ensuring that the original sensitive data is properly encrypted or redacted in a secure manner before any processing.
Option C mentions encrypting the data in SageMaker and using principal component analysis (PCA) to reduce the length of the credit card numbers. While PCA is a dimensionality reduction technique and may help reduce data size, it does not directly address the security of the credit card information. Reducing the length of the credit card numbers could make the data unrecognizable, but it does not ensure compliance with security standards for sensitive data.
Option D provides the best solution. Using AWS KMS (Key Management Service) to encrypt the data on both Amazon S3 and Amazon SageMaker ensures that the data is securely stored and processed. AWS Glue is used to redact the credit card numbers, which means removing or masking the sensitive parts of the data while keeping the data usable for analysis. This method ensures that the credit card information is never exposed in an unencrypted or identifiable form during processing, adhering to best practices for security and compliance in handling sensitive customer information.

Therefore, D is the most secure and compliant approach for protecting the credit card information during the modeling process.

Question No 6:

Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

Therefore, the correct answer is:

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Explanation:

The reason the ML Specialist cannot see the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC is that Amazon SageMaker notebook instances are not directly placed in the VPC by default. Although SageMaker notebook instances are indeed based on EC2 instances, these instances are managed and run by AWS services rather than the customer account, which means they are not directly visible in the customer’s VPC or EC2 management console.

When a SageMaker notebook instance is created, it typically operates outside of the customer's VPC. The notebook instances are provisioned and maintained by Amazon SageMaker using AWS-managed infrastructure. This is why the EBS volumes and EC2 instances associated with the SageMaker notebook instance do not appear in the customer's VPC or EC2 console, as they are not managed directly by the customer.

In order to attach an EBS volume to the SageMaker notebook instance, Amazon provides specific features like direct EBS volume management, which is abstracted to ensure ease of use. However, this means that the EBS volumes themselves might not be visible in the same way as volumes directly associated with instances in the VPC.

Question No 7:

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Correct answer: B

Explanation:

In this scenario, the Machine Learning Specialist wants to review the performance metrics like latency, memory utilization, and CPU utilization while performing load testing on the model endpoint. Among the options, the best approach is to use Amazon CloudWatch to collect and monitor these metrics in real time.

Amazon CloudWatch is a monitoring service designed for observing system performance metrics in AWS. For Amazon SageMaker, CloudWatch automatically tracks a variety of operational metrics such as latency, CPU, memory utilization, and other resource usage that can be monitored in real-time.
By generating a CloudWatch dashboard, the Specialist can aggregate and visualize all these relevant metrics on a single page. This is an efficient way to monitor the system during the load test and ensure that it is scaling effectively based on the workload.
The use of a CloudWatch dashboard provides a centralized view of the performance data, which is essential for making informed decisions about how to configure Auto Scaling for the model endpoint. This allows the Specialist to directly assess the model’s behavior under load and decide on the right scaling policies based on real-time data.
Option A involves analyzing logs from SageMaker stored in Amazon S3 using Amazon Athena and QuickSight, which may not be the most efficient method for real-time monitoring of system metrics like CPU, memory, and latency. This approach is more suited for post-analysis rather than ongoing monitoring during the load test.
Option C and Option D describe using Amazon Elasticsearch (Amazon ES) and Kibana for log analysis. While this can be useful for visualizing logs, it adds unnecessary complexity when compared to using CloudWatch, which is already integrated with SageMaker for performance metrics. Additionally, Elasticsearch and Kibana require more setup and maintenance, whereas CloudWatch provides native support for the metrics the Specialist needs.

In conclusion, B is the most straightforward and efficient solution for reviewing the necessary metrics during the load test.

Question No 8:

Which solution requires the least effort to be able to query both structured and unstructured data stored in an Amazon S3 bucket using SQL?

A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries
B. Use AWS Glue to catalogue the data and Amazon Athena to run queries
C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries
D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Correct answer: B

Explanation:

The task requires the least effort to implement a solution for querying both structured and unstructured data stored in an Amazon S3 bucket. Let's evaluate each option:

Option A (AWS Data Pipeline and Amazon RDS): AWS Data Pipeline is primarily used for managing the movement and transformation of data between different AWS services. While it is a powerful tool for ETL jobs, it requires setting up workflows and managing data movement across services. After transforming the data, using Amazon RDS would require transferring data into a relational database, which involves additional setup and management effort for both database schema design and scaling. This solution is overcomplicated for querying data directly from Amazon S3.
Option B (AWS Glue and Amazon Athena): Amazon Athena is a serverless query service that allows users to run SQL queries directly on data stored in Amazon S3, including both structured and unstructured data, without needing to move the data. AWS Glue is a managed ETL service that can be used to catalogue and transform the data, making it easier to query with Athena. The integration between Athena and Glue allows for automatic schema discovery and management, significantly reducing the operational effort. This solution is highly effective because it leverages serverless services and requires minimal configuration. The data is kept in S3, and queries are executed directly on it.
Option C (AWS Batch and Amazon Aurora): AWS Batch is a managed service for running batch processing jobs, but it is generally not used for real-time querying of data. After performing ETL on the data, you would need to load it into Amazon Aurora, a relational database, which again introduces complexity in schema design and data management. This solution requires more setup and maintenance compared to serverless services like Athena.
Option D (AWS Lambda and Amazon Kinesis Data Analytics): AWS Lambda is typically used for small-scale transformations and event-driven tasks. While it could be used for simple transformations, it is not ideal for running complex ETL jobs on large datasets. Additionally, Amazon Kinesis Data Analytics is designed for real-time streaming data processing, which is not the best match for querying data that is stored statically in S3. This would require more custom setup and orchestration compared to Athena and Glue.

In conclusion, Option B is the most efficient and requires the least effort. Amazon Athena can directly query data in Amazon S3 using SQL, and AWS Glue simplifies data cataloging and schema management. The combination of these two services minimizes the need for infrastructure management and ensures seamless querying, making it the ideal choice for the given scenario.

Question No 9:

Which approach allows the Specialist to use all the data to train the model?

A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset.
C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Correct answer: A

Explanation:

The key requirement in this question is that the Specialist wants to train a model using a large dataset hosted on an Amazon S3 bucket, but the data is too large to fit into the local storage of the Amazon SageMaker notebook instance. The dataset is large, and loading all of it directly onto the notebook instance would be time-consuming and would exceed the capacity of the attached 5 GB Amazon EBS volume.

Option A is the best solution because it addresses this challenge effectively. It suggests loading a smaller subset of the data into the SageMaker notebook instance to verify the training code and model parameters before initiating the actual training job. After confirming that the model and training code are working as expected, the full dataset can be used for training via Pipe input mode in SageMaker. This mode allows data to be streamed directly from S3 to the training instance, avoiding the need to load all the data at once into the notebook or the instance’s local storage. This is a scalable and efficient solution for working with large datasets.

Option B suggests using an Amazon EC2 instance with an AWS Deep Learning AMI and attaching the S3 bucket for training. While this could technically work, it introduces unnecessary complexity. The question specifically mentions using SageMaker for the model training, and this option requires switching to EC2, which is not ideal in this context because the goal is to leverage SageMaker’s built-in capabilities.

Option C introduces AWS Glue, which is a data integration service, but it is not designed for training machine learning models. The focus here should be on using SageMaker to handle the training of the machine learning model, not on using AWS Glue, which is more suited for ETL tasks.

Option D suggests a hybrid approach involving both SageMaker and EC2 with an AWS Deep Learning AMI. While this could work, it complicates the solution by unnecessarily adding an extra EC2 instance to the process. It is more straightforward to use SageMaker with Pipe input mode (as in option A) rather than using EC2 and SageMaker in combination.

In summary, option A is the most effective approach as it utilizes Amazon SageMaker’s Pipe input mode for large-scale data processing directly from S3 without needing to load the entire dataset onto the local storage of the notebook instance, allowing efficient training with large datasets.

Question No 10:

Which approach should the Specialist use for training a model using that data?

A. Write a direct connection to the SQL database within the notebook and pull data in
B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Correct answer: B

Explanation:

The Machine Learning Specialist has completed a proof of concept using a small data sample and is now ready to implement an end-to-end solution on AWS using Amazon SageMaker. The data is stored in Amazon RDS, and the goal is to train a machine learning model using that data.

Option A suggests writing a direct connection to the SQL database within the notebook and pulling the data in. While this may work for smaller datasets, it is not an efficient or scalable solution for larger datasets. Directly querying a SQL database like Amazon RDS within a SageMaker notebook is not optimal because this could lead to performance bottlenecks, especially with large datasets. Querying the database repeatedly during training can slow down the training process, especially if there are network latency issues or high read loads.

Option B recommends pushing the data from the SQL database to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook. This approach is the most efficient for several reasons. Amazon S3 is designed to store large datasets cost-effectively and efficiently, and it integrates seamlessly with Amazon SageMaker. By transferring the data to S3, the Specialist can use SageMaker’s data input modes (like File or Pipe input mode) for easy access to large datasets during model training. This is a common best practice for scaling machine learning workloads, as it decouples the data storage from the compute resources required for model training. AWS Data Pipeline can automate the data transfer process, ensuring that the data is available in S3 for training in a reliable and consistent manner.

Option C suggests moving the data to Amazon DynamoDB and setting up a connection to DynamoDB within the notebook to pull data in. While DynamoDB is a highly scalable and low-latency NoSQL database, it is not ideal for storing large amounts of structured data typically used in machine learning models. DynamoDB is optimized for fast read and write operations on relatively small, non-relational datasets, but it may not be efficient for large-scale data used for training machine learning models. Therefore, this approach is not suitable for the given scenario.

Option D recommends moving the data to Amazon ElastiCache using AWS DMS (Database Migration Service) and setting up a connection within the notebook for fast access. Amazon ElastiCache is primarily used for caching and improving the performance of database-driven applications by storing frequently accessed data in memory. It is not suitable for training machine learning models, especially with large historical datasets. ElastiCache is optimized for low-latency data access but not for large-scale data storage, making it an impractical choice for model training.

In conclusion, option B is the best approach because it leverages Amazon S3, which is a scalable, cost-effective, and flexible storage solution for large datasets. This allows the Specialist to efficiently train a machine learning model using Amazon SageMaker by storing the data in S3 and providing the S3 location in the notebook. This solution ensures that the data is accessible without performance bottlenecks, and it aligns with best practices for end-to-end machine learning workflows in AWS.