Quiz IT certification exam

Question 1

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?

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Accepted Answer

Use an autoencoder or principal component analysis (PCA) to replace original features with new features

Answer

Run self-correlation on all features and remove highly correlated features

Answer

Normalize all numerical values to be between 0 and 1

Answer

Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Question 2

A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable ecall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.Which techniques should be used to meet these requirements?

Accepted Answer

Add class weights to the MLP's loss function and then retrain

Answer

Gather more data using Amazon Mechanical Turk and then retrain

Answer

Train an anomaly detection model instead of an MLP

Answer

Train an XGBoost model instead of an MLP

Question 3

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.How should the Specialist frame this business problem?

Accepted Answer

Binary classification

Answer

Streaming classification

Answer

Multi-category classification

Answer

Regression classification

Question 4

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.Which model will meet the business requirement?

Accepted Answer

Linear regression

Answer

Logistic regression

Answer

K-means

Answer

Principal component analysis (PCA)

Question 5

A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

Accepted Answer

Configure the training program as an ENTRYPOINT named train

Answer

Modify the bash_profile file in the container and add a bash command to start the training program

Answer

Use CMD config in the Dockerfile to add the training program as a CMD of the image

Answer

Copy the training program to directory /opt/ml/train

Question 6

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Accepted Answer

Numerical value binning

Accepted Answer

Logarithmic transformation

Answer

Cross-validation

Answer

High-degree polynomial transformation

Answer

One hot encoding

Question 7

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.What approach should the Specialist take to accomplish these tasks?

Accepted Answer

Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.

Answer

Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

Answer

Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

Answer

Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

Question 8

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use SpotInstances in the EMR cluster.Which nodes should the Specialist launch on Spot Instances?

Accepted Answer

Any of the task nodes

Answer

Master node

Answer

Any of the core nodes

Answer

Both core and task nodes

Question 9

A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

Accepted Answer

Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Answer

Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

Answer

Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

Answer

Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.

Question 10

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.What changes should the Specialist consider to solve this issue? (Choose three.)

Accepted Answer

Choose a lower number of layers

Accepted Answer

Enable dropout

Accepted Answer

Enable early stopping

Answer

Choose a higher number of layers

Answer

Choose a smaller learning rate

Answer

Include all the images from the test set in the training set

Question 11

A Machine Learning Specialist is attempting to build a linear regression model.Given the displayed residual plot only, what is the MOST likely problem with the model?

Accepted Answer

Linear regression is inappropriate. The residuals do not have constant variance.

Answer

Linear regression is inappropriate. The underlying data has outliers.

Answer

Linear regression is appropriate. The residuals have a zero mean.

Answer

Linear regression is appropriate. The residuals have constant variance.

Question 12

A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.The company requires at least 85% accuracy to make use of the model.After an exhaustive grid search, the optimal hyperparameters produced the following:68% accuracy on the training set67% accuracy on the validation setWhat can the machine learning specialist do to improve the system's accuracy?

Accepted Answer

Add more data to the training set and retrain the model using transfer learning to reduce the bias.

Answer

Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

Answer

Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Answer

Train a new model using the current neural network architecture.

Question 13

A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of1,000 hand-labeled images covering 10 distinct items. The training results were poor.Which machine learning approach fulfills the company's long-term needs?

Accepted Answer

Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

Answer

Convert the images to grayscale and retrain the model

Answer

Reduce the number of distinct items from 10 to 2, build the model, and iterate

Answer

Attach different colored labels to each item, take the images again, and build the model

Question 14

A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.Which cross-validation strategy should the Data Scientist adopt?

Accepted Answer

A stratified k-fold cross-validation strategy with k=5

Answer

A k-fold cross-validation strategy with k=5

Answer

A k-fold cross-validation strategy with k=5 and 3 repeats

Answer

An 80/20 stratified split between training and validation

Question 15

A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.Which architecture should be used to scale the solution at the lowest cost?

Accepted Answer

Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

Answer

Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task

Answer

Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler

Answer

Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler

Question 16

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.Which is the FASTEST route to index the assets?

Accepted Answer

Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.

Answer

Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.

Answer

Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.

Answer

Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

Question 17

A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

Accepted Answer

1 shards

Answer

10 shards

Answer

100 shards

Answer

1,000 shards

Question 18

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.Which model describes the underlying data in this situation?

Accepted Answer

A full Bayesian network, since some of the features are statistically dependent.

Answer

A naive Bayesian model, since the features are all conditionally independent.

Answer

A full Bayesian network, since the features are all conditionally independent.

Answer

A naive Bayesian model, since some of the features are statistically dependent.

Question 19

A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.<img alt="Amazon MLS-C01 image Question 179 4743 09162024005655000000"   width="622" height="383" src="https://examgecko.com/getfile/8459b58a-aa85-4df9-bb81-ad036fd11a44">What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

Accepted Answer

Logarithmic transformation

Answer

Exponential transformation

Answer

Polynomial transformation

Answer

Sinusoidal transformation

Question 20

A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.<img alt="Amazon MLS-C01 image Question 180 4744 09162024005655000000"   width="759" height="230" src="https://examgecko.com/getfile/c6838c55-0b8d-408d-8a30-37071f17c654">Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

Accepted Answer

Lower the max_depth parameter value.

Answer

Increase the max_depth parameter value.

Answer

Update the objective to binary:logistic.

Answer

Lower the min_child_weight parameter value.

Question 21

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.The solution needs to do the following:Calculate an anomaly score for each web traffic entry.Adapt unusual event identification to changing web patterns over time.Which approach should the data scientist implement to meet these requirements?

Accepted Answer

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

Answer

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.

Answer

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.

Answer

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

Question 22

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.What type of machine learning model should be used?

Accepted Answer

Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

Answer

Classification month-to-month using supervised learning of the 200 categories based on claim contents.

Answer

Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Answer

Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Question 23

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.Which solution satisfies these requirements with MINIMAL effort?

Accepted Answer

Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

Answer

Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

Answer

Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.

Answer

Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

Question 24

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.Which approach should a Machine Learning Specialist take to obtain accurate predictions?

Accepted Answer

Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

Answer

Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

Answer

Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

Answer

Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

Question 25

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.Which deployment architecture for the model will address these business requirements?

Accepted Answer

Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.

Answer

Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.

Answer

Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

Answer

Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.

Question 26

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

Accepted Answer

Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

Answer

Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.

Answer

Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.

Answer

Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Question 27

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.Which machine learning algorithm should the researchers use that BEST meets their requirements?

Accepted Answer

Convolutional neural network (CNN)

Answer

Latent Dirichlet Allocation (LDA)

Answer

Recurrent neural network (RNN)

Answer

K-means

Question 28

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.How should a data scientist adjust the solution?

Accepted Answer

Use the event tracker in Amazon Personalize to include real-time user interactions.

Answer

Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

Answer

Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

Answer

Add event type and event value fields to the interactions dataset in Amazon Personalize.

Question 29

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

Accepted Answer

Modify the security group on the endpoint network interface to restrict access to the instances.

Accepted Answer

Add a SageMaker Runtime VPC endpoint interface to the VPC.

Answer

Add a VPC endpoint policy to allow access to the IAM users.

Answer

Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.

Answer

Modify the ACL on the endpoint network interface to restrict access to the instances.

Question 30

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.How should a machine learning specialist meet these requirements?

Accepted Answer

Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.

Answer

Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.

Answer

Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.

Answer

Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

Question 31

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

Accepted Answer

Set PerformAutoML to true.

Accepted Answer

Set PerformHPO to true.

Answer

Set ForecastHorizon to 4.

Answer

Set ForecastFrequency to W for weekly.

Answer

Set FeaturizationMethodName to filling.

Question 32

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.<img alt="Amazon MLS-C01 image Question 192 4756 09162024005655000000"   width="640" height="105" src="https://examgecko.com/getfile/d355a872-9d25-407b-98aa-da86b99dd417">How should the data scientist transform the data?

Accepted Answer

Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

Answer

Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.

Answer

Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.

Answer

Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

Question 33

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.Which architecture changes would ensure that provisioned resources are being utilized effectively?

Accepted Answer

Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

Answer

Redeploy the model as a batch transform job on an M5 instance.

Answer

Redeploy the model on a P3dn instance.

Answer

Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Question 34

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?

Accepted Answer

Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Answer

Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

Answer

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.

Answer

Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

Question 35

A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.Which strategy will allow the data scientist to identify fraudulent accounts?

Accepted Answer

Create a FindMatches machine learning transform in AWS Glue.

Answer

Execute the built-in FindDuplicates Amazon Athena query.

Answer

Create an AWS Glue crawler to infer duplicate accounts in the source data.

Answer

Search for duplicate accounts in the AWS Glue Data Catalog.

Question 36

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.<img alt="Amazon MLS-C01 image Question 196 4760 09162024005655000000"   width="170" height="59" src="https://examgecko.com/getfile/7932d3a4-9734-452d-983a-973ea02b8187">Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

Accepted Answer

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Accepted Answer

Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

Answer

Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).

Answer

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Answer

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Question 37

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.Which action will resolve the problem?

Accepted Answer

Adjust hyperparameters related to the attention mechanism.

Answer

Change preprocessing to use n-grams.

Answer

Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.

Answer

Choose a different weight initialization type.

Question 38

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.Which metrics should the data scientist use to optimize the model? (Choose two.)

Accepted Answer

Area under the precision-recall curve

Accepted Answer

True positive rate

Answer

Specificity

Answer

False positive rate

Answer

Accuracy

Question 39

A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.Which action will provide the MOST secure protection?

Accepted Answer

Enable network isolation for training jobs.

Answer

Remove Amazon S3 access permissions from the SageMaker execution role.

Answer

Encrypt the weights of the CNN model.

Answer

Encrypt the training and validation dataset.

Question 40

A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

Accepted Answer

Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.

Answer

Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.

Answer

Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.

Answer

Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.

Amazon MLS-C01 Practice Test 5

Amazon MLS-C01 Practice Tests