[Dec-2021] Professional-Machine-Learning-Engineer Pre-Exam Practice Tests | Exam Questions and Answers for Google Certification Study Guide
Google Professional Machine Learning Engineer Certification Sample Questions
NEW QUESTION 43
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?
- A. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
- B. Redeploy the model as a batch transform job on an M5 instance.
- C. Redeploy the model on a P3dn instance.
- D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
Answer: D
NEW QUESTION 44
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 recall 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?
- A. Gather more data using Amazon Mechanical Turk and then retrain
- B. Train an XGBoost model instead of an MLP
- C. Add class weights to the MLP's loss function and then retrain
- D. Train an anomaly detection model instead of an MLP
Answer: B
NEW QUESTION 45
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
- A. Reduce the image shape
- B. Change the optimizer
- C. Change the learning rate
- D. Reduce the batch size
Answer: B
NEW QUESTION 46
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
- A. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- B. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
- C. Create a new view with BigQuery that does not include a column with city information
- D. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
Answer: D
NEW QUESTION 47
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 48
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 Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
- B. 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.
- C. 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.
- D. 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.
Answer: C
NEW QUESTION 49
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that all hyperparameters are tuned
- B. Ensure that feature expectations are captured in the schema
- C. Ensure that training is reproducible
- D. Ensure that model performance is monitored
Answer: A
NEW QUESTION 50
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
- A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
- B. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
- C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- D. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
Answer: D
NEW QUESTION 51
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?
- A. Copy the training program to directory /opt/ml/train
- B. Configure the training program as an ENTRYPOINTnamed train
- C. Modify the bash_profile file in the container and add a bashcommand to start the training program
- D. Use CMD configin the Dockerfile to add the training program as a CMD of the image
Answer: D
NEW QUESTION 52
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 53
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 54
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 55
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 56
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 a recommendation engine 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 regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- D. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
Answer: A
NEW QUESTION 57
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
- A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- B. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
- C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
- D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
Answer: D
NEW QUESTION 58
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 59
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 area under the precision-recall curve (AUC PR) value
- 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 Precision at a Recall value of 0.50
Answer: A
NEW QUESTION 60
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
- Assessing data readiness
- Define business success criteria
- Assessing and communicating business impact
- Defining business problems
- Identifying nonML solutions
- Aligning with Google AI principles and practices (e.g. different biases)
- Determination of when a model is deemed unsuccessful
- Define ML problem
- Identifying data sources
- Defining output use
- Managing incorrect results
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Success metrics
- Defining the input (features) and predicted output format
- Assessing ML solution readiness
- Defining problem type (classification, regression, clustering, etc.)
- Key results
- Defining outcome of model predictions
Topics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer dumps pdf will include the following topics:
- ML Model Development
- ML Problem Framing
- ML Solution Monitoring, Optimization, and Maintenance
- ML Solution Architecture
- ML Pipeline Automation & Orchestration
- Data Preparation and Processing
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