Professional-Machine-Learning-Engineer Exam Questions Dumps, Selling Google Products
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
Design pipeline. Considerations include:
- Organization and tracking experiments and pipeline runs
- Setup of trigger and pipeline schedule
- A/B and canary testing
- Storing data and generated artifacts
- Orchestration framework
- Hooking into model and dataset versioning
- Identification of components, parameters, triggers, and compute needs
- Decoupling components with Cloud Build
- Hooking models into existing CI/CD deployment system
- Use CI/CD to test and deploy models
- Google Cloud serving options
- Performing data validation
- Model/dataset lineage
- Hybrid or multi-cloud strategies
- Implement serving pipeline
- Testing for target performance
- Tuning compute performance
- Implement training pipeline
- Track and audit metadata
- Constructing and testing of parameterized pipeline definition in SDK
- Model binary options
NEW QUESTION 11
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.)
- A. Modify the ACL on the endpoint network interface to restrict access to the instances.
- B. Modify the security group on the endpoint network interface to restrict access to the instances.
- C. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
- D. Add a VPC endpoint policy to allow access to the IAM users.
- E. Add a SageMaker Runtime VPC endpoint interface to the VPC.
Answer: B,D
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/
NEW QUESTION 12
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 batch size' parameter
- B. Modify the 'epochs' parameter
- C. Modify the 'scale-tier' parameter
- D. Modify the 'learning rate' parameter
Answer: B
NEW QUESTION 13
A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.
Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?
- A. Increase the randomization of training data in the mini-batches used in training
- B. Apply L1 or L2 regularization and dropouts to the training
- C. Reduce the number of layers and units (or neurons) from the deep learning network
- D. Allocate a higher proportion of the overall data to the training dataset
Answer: C
NEW QUESTION 14
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
- B. Import your user events and then your product catalog to make sure you have the highest quality event stream
- C. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- D. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
Answer: B
NEW QUESTION 15
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? (Select TWO.)
- A. The Random Cut Forest (RCF) algorithm
- B. The Latent Dirichlet Allocation (LDA) algorithm
- C. The factorization machines (FM) algorithm
- D. The k-means algorithm
- E. The principal component analysis (PCA) algorithm
Answer: D,E
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION 16
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?
- A. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
- B. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
- C. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
- D. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
Answer: D
NEW QUESTION 17
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
- A. Automate multiple training runs using Cloud Composer
- B. Create multiple models using AutoML Tables
- C. Create an experiment in Kubeflow Pipelines to organize multiple runs
- D. Run multiple training jobs on Al Platform with similar job names
Answer: D
NEW QUESTION 18
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. Organize the Docker container's file structure to execute on GPU instances.
- B. Bundle the NVIDIA drivers with the Docker image.
- C. Build the Docker container to be NVIDIA-Docker compatible.
- D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
Answer: B
NEW QUESTION 19
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. Use Al Platform for distributed training
- C. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
- D. Create a Managed Instance Group with autoscaling
Answer: C
NEW QUESTION 20
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
- A. Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue. - B. Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction - C. Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - D. Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
Answer: C
NEW QUESTION 21
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 layers for the neural network.
- C. Increase the number of epochs for model training
- D. Increase the dropout rate for the second-to-last layer.
Answer: C
NEW QUESTION 22
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory dat a. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?
- A. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
- B. Use the TFX ModelValidator tools to specify performance metrics for production readiness
- C. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
- D. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
Answer: B
NEW QUESTION 23
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 a Cloud Function 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 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
Answer: B
NEW QUESTION 24
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. 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.
- B. 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
- C. 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
- 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: B
NEW QUESTION 25
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?
- A. Federated learning
- B. Data Loss Prevention API
- C. MD5 to encrypt data
- D. Differential privacy
Answer: A
NEW QUESTION 26
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Decrease the maximum number of trials during subsequent training phases.
- B. Set the early stopping parameter to TRUE
- C. Decrease the range of floating-point values
- D. Decrease the number of parallel trials
- E. Change the search algorithm from Bayesian search to random search.
Answer: A,E
NEW QUESTION 27
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