NEW QUESTION 1
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

• A. The model might have prediction errors over a range of target values.
• B. The dataset cannot be accurately represented using the regression model
• C. There are too many variables in the model
• D. The model is predicting its target values perfectly.

Answer: D

NEW QUESTION 2
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 be fraudulent
How should the Specialist frame this business problem'?

• A. Streaming classification
• B. Binary classification
• C. Multi-category classification
• D. Regression classification

Answer: A

NEW QUESTION 3
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure? 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.

• A. 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.
• B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP
• C. 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

Answer: C

NEW QUESTION 4
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches
What actions would allow the Specialist to get relevant numerical representations?

• A. Reduce image resolution and use reduced resolution pixel values as features
• B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
• C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
• D. Average colors by channel to obtain three-dimensional representations of images.

Answer: A

NEW QUESTION 5
A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

• A. Logistic regression
• B. Random Cut Forest (RCF)
• C. Principal component analysis (PCA)
• D. Linear regression

Answer: B

NEW QUESTION 6
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the number of epochs for model training.
• C. Increase the number of layers for the neural network.
• D. Increase the dropout rate for the second-to-last layer.

Answer: B

NEW QUESTION 7
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning
The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:
• Solution simplicity
• Fast development time
• Low cost
• High flexibility
What technologies meet the company's requirements?

• A. Amazon S3 and Amazon Athena
• B. Amazon Redshift and AWS Glue
• C. Amazon DynamoDB and DynamoDB Accelerator (DAX)
• D. Amazon RDS and Amazon ES

Answer: B

NEW QUESTION 8
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

• A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%
• B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%
• C. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).
• D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: A

NEW QUESTION 9
For the given confusion matrix, what is the recall and precision of the model?

• A. Recall = 0.92 Precision = 0.84
• B. Recall = 0.84 Precision = 0.8
• C. Recall = 0.92 Precision = 0.8
• D. Recall = 0.8 Precision = 0.92

Answer: A

NEW QUESTION 10
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

• A. Bundle the NVIDIA drivers with the Docker image.
• B. Build the Docker container to be NVIDIA-Docker compatible.
• C. Organize the Docker container's file structure to execute on GPU instances.
• D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

Answer: A

NEW QUESTION 11
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?

• A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
• D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: B

NEW QUESTION 12
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker
When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?
Choose the maximum number of hyperparameters supported by

• A. Amazon SageMaker to search the largest number of combinations possible
• B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
• C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
• D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

Answer: C

NEW QUESTION 13
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

• A. Drop all records from the dataset where age has been set to 0.
• B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
• C. Drop the age feature from the dataset and train the model using the rest of the features.
• D. Use k-means clustering to handle missing features.

Answer: A

NEW QUESTION 14
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training
What should the Specialist do to optimize the data for training on SageMaker'?

• A. Use the SageMaker batch transform feature to transform the training data into a DataFrame
• B. Use AWS Glue to compress the data into the Apache Parquet format
• C. Transform the dataset into the Recordio protobuf format
• D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

Answer: C

NEW QUESTION 15
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

• A. Create one-hot word encoding vectors.
• B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
• C. Create word embedding factors that store edit distance with every other word.
• D. Download word embedding’s pre-trained on a large corpus.

Answer: A

NEW QUESTION 16
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

• A. Download the AWS SDK for the Spark environment
• B. Install the SageMaker Spark library in the Spark environment.
• C. Use the appropriate estimator from the SageMaker Spark Library to train a model.
• D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
• E. Use the sageMakerMode
• F. transform method to get inferences from the model hosted in SageMaker
• G. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

Answer: DEF

NEW QUESTION 17
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days
Which of the following modeling techniques should the Specialist use1?

• A. Time-series prediction
• B. Anomaly detection
• C. Binary classification
• D. Regression

Answer: D

NEW QUESTION 18
A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5
Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?

• A. 10
• B. 30
• C. 600
• D. 2,400

Answer: C

NEW QUESTION 19
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model
What should the Specialist do to address the performance issues with the current solution?

• A. Use the SageMaker batch transform feature
• B. Compress the training data into Apache Parquet format.
• C. Ensure that the input mode for the training job is set to Pipe.
• D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Answer: B

NEW QUESTION 20
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

• A. . m5 4xlarge (general purpose)
• B. r5.2xlarge (memory optimized)
• C. p3.2xlarge (GPU accelerated computing)
• D. p3 8xlarge (GPU accelerated computing)

Answer: C

NEW QUESTION 21
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users
The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.1 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns
Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

• A. Add more deep trees to the random forest to enable the model to learn more features.
• B. indicate a copy of the samples in the test database in the training dataset
• C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• D. Change the cost function so that false negatives have a higher impact on the cost value than false positives
• E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: BD

NEW QUESTION 22
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• 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:
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

NEW QUESTION 23
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant
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 OuickSight 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 data as it is generated by Amazon SageMaker
• D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

Answer: B

NEW QUESTION 24
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