Amazon AWS Certified Machine Learning - Specialty
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#131 (Accuracy: 100% / 3 votes)
A data scientist wants to build a financial trading bot to automate investment decisions. The financial bot should recommend the quantity and price of an asset to buy or sell to maximize long-term profit. The data scientist will continuously stream financial transactions to the bot for training purposes. The data scientist must select the appropriate machine learning (ML) algorithm to develop the financial trading bot.

Which type of ML algorithm will meet these requirements?
  • A. Supervised learning
  • B. Unsupervised learning
  • C. Semi-supervised learning
  • D. Reinforcement learning
#132 (Accuracy: 100% / 5 votes)
A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?
  • A. Amazon SageMaker DeepAR forecasting algorithm
  • B. Amazon SageMaker XGBoost algorithm
  • C. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
  • D. A convolutional neural network (CNN) and ResNet
#133 (Accuracy: 100% / 5 votes)
A company operates an amusement park. The company wants to collect, monitor, and store real-time traffic data at several park entrances by using strategically placed cameras. The company’s security team must be able to immediately access the data for viewing. Stored data must be indexed and must be accessible to the company’s data science team.

Which solution will meet these requirements MOST cost-effectively?
  • A. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in integration with Amazon Rekognition for viewing by the security team.
  • B. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.
  • C. Use Amazon Rekognition Video and the GStreamer plugin to ingest the data for viewing by the security team. Use Amazon Kinesis Data Streams to index and store the data.
  • D. Use Amazon Kinesis Data Firehose to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.
#134 (Accuracy: 100% / 4 votes)
A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.

How should the company prepare the data for the model to improve the model's accuracy?
  • A. Adjust the class weight to account for each machine type.
  • B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
  • C. Undersample the non-failure events. Stratify the non-failure events by machine type.
  • D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).
#135 (Accuracy: 93% / 7 votes)
A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 ׀¢׀’ in size.
The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.
Which solution meets these requirements?
  • A. Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
  • B. Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
  • C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
  • D. Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.
#136 (Accuracy: 100% / 5 votes)
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
  • A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  • B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  • C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
#137 (Accuracy: 100% / 8 votes)
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:

TransactionTimestamp (Timestamp)
CardName (Varchar)
CardNo (Varchar)

The data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a TransactionTime column. Finally, the CardName column must be renamed to NameOnCard.

The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution also must be automated and must minimize the load on the Amazon Redshift cluster.

Which solution meets these requirements?
  • A. Set up an Amazon EMR cluster. Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
  • B. Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J, to query the data from the Amazon Redshift cluster directly Export the resulting dataset into a file. Upload the file into the S3 bucket. Perform these tasks monthly.
  • C. Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination. Use the built-in transforms Filter, Map, and RenameField to perform the required transformations. Schedule the job to run monthly.
  • D. Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly.
#138 (Accuracy: 93% / 8 votes)
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?
  • A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
  • B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
  • D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
#139 (Accuracy: 100% / 10 votes)
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
  • A. Poisson distribution
  • B. Uniform distribution
  • C. Normal distribution
  • D. Binomial distribution
#140 (Accuracy: 100% / 4 votes)
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.

Which change will create the required transformed records with the LEAST operational overhead?
  • A. Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.
  • B. Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
  • C. Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.
  • D. Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.