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NEW QUESTION 35
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
- A. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- B. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
- C. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- D. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
Answer: D
NEW QUESTION 36
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?
- A. Confusion matrix
- B. Root Mean Square Error (RMSE)
- C. Residual plots
- D. Area under the curve
Answer: D
NEW QUESTION 37
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?
- A. Modify the 'scale-tier' parameter
- B. Modify the 'learning rate' parameter
- C. Modify the batch size' parameter
- D. Modify the 'epochs' parameter
Answer: C
NEW QUESTION 38
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
- A. Categorical cross-entropy
- B. Categorical hinge
- C. Binary cross-entropy
- D. Sparse categorical cross-entropy
Answer: C
NEW QUESTION 39
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- B. Reduce the learning rate and run the training process until the training loss stops decreasing.
- C. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
- D. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
Answer: B
NEW QUESTION 40
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)
- A. The k-means algorithm
- B. The Latent Dirichlet Allocation (LDA) algorithm
- C. The Random Cut Forest (RCF) algorithm
- D. The principal component analysis (PCA) algorithm
- E. The factorization machines (FM) algorithm
Answer: A,D
Explanation:
Explanation/Reference:
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION 41
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A. A DeepAR forecasting model based on a recurrent neural network (RNN)
- B. AnXGBoost model where the objective parameter is set to multi:softmax
- C. A regression forest where the number of trees is set equal to the number of product categories
- D. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
Answer: D
NEW QUESTION 42
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
- A. Split the training and test data based on time rather than a random split to avoid leakage
- B. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- C. Normalize the data for the training, and test datasets as two separate steps.
- D. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: D
NEW QUESTION 43
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- B. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- C. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
- D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
Answer: C
NEW QUESTION 44
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?
- A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
- B. Initialize the model with random weights in all layers and replace the last fully connected layer.
- C. Initialize the model with random weights in all layers including the last fully connected layer.
- D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
Answer: D
NEW QUESTION 45
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?
- A. Increase the number of false positives
- B. Increase the recall
- C. Decrease the recall.
- D. Decrease the number of false negatives
Answer: D
NEW QUESTION 46
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?
- A. Modify the 'scale-tier' parameter
- B. Modify the batch size' parameter
- C. Modify the 'learning rate' parameter
- D. Modify the 'epochs' parameter
Answer: D
NEW QUESTION 47
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?
- A. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
- B. AWS Glue with a custom ETL script to transform the data.
- C. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
- D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: C
Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/
NEW QUESTION 48
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?
- A. Amazon Comprehend syntax analysis and entity detection
- B. Natural Language Toolkit (NLTK) stemming and stop word removal
- C. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
- D. Amazon SageMaker BlazingText cbowmode
Answer: C
Explanation:
Explanation/Reference: https://monkeylearn.com/sentiment-analysis/
NEW QUESTION 49
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
- A. Create a cluster on Dataproc for training
- B. Create a Managed Instance Group with autoscaling
- C. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
- D. Use Al Platform for distributed training
Answer: B
NEW QUESTION 50
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?
- A. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map - B. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome. - C. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction - D. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
Answer: D
NEW QUESTION 51
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address data leakage by applying nested cross-validation during model training.
- B. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- C. Address the model overfitting by using a less complex algorithm.
- D. Address data leakage by removing features highly correlated with the target value.
Answer: A
NEW QUESTION 52
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
- A. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
Answer: C
Explanation:
Explanation/Reference:
NEW QUESTION 53
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?
- A. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map - B. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome. - C. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction - D. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
Answer: B
NEW QUESTION 54
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?
- A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
- B. Initialize the model with random weights in all layers and replace the last fully connected layer.
- C. Initialize the model with random weights in all layers including the last fully connected layer.
- D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION 55
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?
- A. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
- B. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
- C. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
- D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
Answer: D
NEW QUESTION 56
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?
- A. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
- B. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- C. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
- D. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
Answer: C
NEW QUESTION 57
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?
- A. Ensure that you have GPU quota in the selected region.
- B. Ensure that the required GPU is available in the selected region.
- C. Ensure that the selected GPU has enough GPU memory for the workload.
- D. Ensure that you have preemptible GPU quota in the selected region.
Answer: A
NEW QUESTION 58
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
- A. Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- B. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting
- D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Answer: B
NEW QUESTION 59
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- C. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
- D. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
Answer: D
NEW QUESTION 60
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