Updated Jan-2022 Test Engine to Practice Professional-Machine-Learning-Engineer Dumps & Practice Exam
Dumps Collection Professional-Machine-Learning-Engineer Test Engine Dumps Training With 72 Questions
NEW QUESTION 27
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Federated learning, reproducibility, and explainability
- B. Differential privacy federated learning, and explainability
- C. Redaction, reproducibility, and explainability
- D. Traceability, reproducibility, and explainability
Answer: D
NEW QUESTION 28
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
- A. 1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold - B. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold - C. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold - D. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
Answer: D
NEW QUESTION 29
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
- A. AutoML Vision Edge mobile-low-latency-1 model
- B. AutoML Vision Edge mobile-versatile-1 model
- C. AutoML Vision model
- D. AutoML Vision Edge mobile-high-accuracy-1 model
Answer: C
NEW QUESTION 30
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)
- A. AWS CloudTrail
- B. Amazon CloudWatch
- C. AWS Health
- D. AWS Config
- E. AWS Trusted Advisor
Answer: A,B
Explanation:
Explanation/Reference: https://aws.amazon.com/sagemaker/faqs/
NEW QUESTION 31
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
- A. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
- B. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
- C. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
- D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
Answer: A
NEW QUESTION 32
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+128*2=161408
- B. 500*256+256*128+128*2 = 161024
- C. 501*256+257*128+2 = 161154
- D. 500*256*0 25+256*128*0 25+128*2 = 40448
Answer: D
NEW QUESTION 33
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- B. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- D. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
Answer: A
NEW QUESTION 34
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?
- A. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- B. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- C. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- D. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Answer: C
Explanation:
Explanation/Reference: https://aws.amazon.com/step-functions/use-cases/
NEW QUESTION 35
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 these requirements?
- A. 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.
- B. 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.
- C. 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.
- 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: A
NEW QUESTION 36
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
- A. Incorrect data split ratio during model training, evaluation, validation, and test
- B. Poor data quality
- C. Lack of model retraining
- D. Too few layers in the model for capturing information
Answer: A
NEW QUESTION 37
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?
- A. An optimization objective that maximizes the Precision at a Recall value of 0.50
- B. An optimization objective that minimizes Log loss
- C. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
- D. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
Answer: D
NEW QUESTION 38
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?
- A. Streaming classification
- B. Multi-category classification
- C. Binary classification
- D. Regression classification
Answer: B
NEW QUESTION 39
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Linear regression
- B. Reinforcement learning
- C. Clustering
- D. Classification
Answer: D
Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?
Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html
NEW QUESTION 40
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Configure AutoML Tables to perform the classification task
- B. Run a BigQuery ML task to perform logistic regression for the classification
- C. Use Al Platform Notebooks to run the classification model with pandas library
- D. Use Al Platform to run the classification model job configured for hyperparameter tuning
Answer: C
NEW QUESTION 41
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?
- A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
- B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
- C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
- D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
Answer: C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html
NEW QUESTION 42
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. Root Mean Square Error (RMSE)
- B. Area under the curve
- C. Residual plots
- D. Confusion matrix
Answer: B
NEW QUESTION 43
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?
- A. The model's hyperparameters should be periodically updated to prevent drift.
- B. The model should be periodically retrained using the original training data plus new data as product inventory changes.
- C. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
- D. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
Answer: B
NEW QUESTION 44
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. Download word embeddings pre-trained on a large corpus.
- B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
- C. Create word embedding vectors that store edit distance with every other word.
- D. Create one-hot word encoding vectors.
Answer: D
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION 45
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A. Write your data in TFRecords.
- B. Use one-hot encoding on all categorical features.
- C. Oversample the fraudulent transaction 10 times.
- D. Z-normalize all the numeric features.
Answer: C
NEW QUESTION 46
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. 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. - B. 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 - C. 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. - D. 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
Answer: A
NEW QUESTION 47
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