Professional-Machine-Learning-Engineer Dumps with Practice Exam Questions Answers
Professional-Machine-Learning-Engineer by Google Cloud Certified Actual Free Exam Practice Test
Google Professional Machine Learning Engineer Certification Exam is a test designed to validate advanced skills in machine learning. Professional-Machine-Learning-Engineer exam is intended for professionals who want to demonstrate their ability to design and develop machine learning models on the Google Cloud platform. Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their expertise in developing and deploying machine learning models.
Google Professional Machine Learning Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals in the field of machine learning. Professional-Machine-Learning-Engineer exam is intended for individuals who have a strong understanding of machine learning concepts, including supervised learning, unsupervised learning, and deep learning. Additionally, this certification exam assesses an individual's ability to design and implement machine learning models on the Google Cloud Platform.
Google Professional Machine Learning Engineer certification is highly valued in the industry and can lead to excellent career opportunities for individuals with expertise in this field. Google Professional Machine Learning Engineer certification is a testament to a candidate's ability to design, develop, and deploy machine learning models, and it can be a valuable asset for anyone seeking a career in machine learning or data science. Additionally, the certification demonstrates a candidate's knowledge of Google Cloud technologies and their ability to use them effectively to solve real-world problems.
NEW QUESTION # 30
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 feature expectations are captured in the schema
- B. Ensure that all hyperparameters are tuned
- C. Ensure that training is reproducible
- D. Ensure that model performance is monitored
Answer: B
NEW QUESTION # 31
Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?
- A. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
- B. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
- C. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.
- D. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
Answer: A
NEW QUESTION # 32
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?
- A. Create alerts to monitor for skew, and retrain the model.
- B. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
- C. Perform feature selection on the model, and retrain the model with fewer features
- D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
Answer: A
Explanation:
Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew
NEW QUESTION # 33
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?
- A. Rate of return as measured by additional revenue generated minus the cost of developing a new model
- B. User engagement as measured by the number of battles played daily per user
- C. Precision and recall of assigning players to teams based on their predicted versus actual ability
- D. Average time players wait before being assigned to a team
Answer: B
NEW QUESTION # 34
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 the average of all account balance predictions drops 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. 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. - D. 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
Answer: C
NEW QUESTION # 35
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. Use one-hot encoding on all categorical features.
- B. Oversample the fraudulent transaction 10 times.
- C. Write your data in TFRecords.
- D. Z-normalize all the numeric features.
Answer: B
NEW QUESTION # 36
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. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- D. Normalize the data for the training, and test datasets as two separate steps.
Answer: C
NEW QUESTION # 37
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Significantly increase the max_batch_size TensorFlow Serving parameter
- B. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- C. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- D. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
Answer: A
NEW QUESTION # 38
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
- A. Enable early stopping in your Vertex AI Training job.
- B. Increase the instance memory to 512 GB and increase the batch size.
- C. Use the tf.distribute.Strategy API and run a distributed training job.
- D. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.
Answer: A
NEW QUESTION # 39
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?
- A. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
- B. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.
- C. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
- D. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
Answer: C
NEW QUESTION # 40
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
- A. Reinforcement learning
- B. Convolutional Neural Networks (CNN)
- C. Recurrent Neural Networks (RNN)
- D. Recommender system
Answer: B
Explanation:
Convolutional Neural Networks (CNNs) are well-suited to image classification tasks such as identifying defects in products based on images. CNNs use convolutional layers that effectively extract features from images and can be trained to identify patterns in the images. The architecture of CNNs is optimized for image processing tasks and can be more efficient for extracting features from images than other types of neural networks.
Reinforcement learning is a type of machine learning that is used for problems with a delayed reward, such as game playing or robotics. Recommender system is used for recommending products or content to users based on their preferences. Recurrent Neural Networks (RNNs) are used for sequential data such as time series or natural language processing.
NEW QUESTION # 41
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. Lack of model retraining
- B. Poor data quality
- C. Incorrect data split ratio during model training, evaluation, validation, and test
- D. Too few layers in the model for capturing information
Answer: C
NEW QUESTION # 42
A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?
- A. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
- B. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
- C. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
- D. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
Answer: A
NEW QUESTION # 43
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. Differential privacy
- B. MD5 to encrypt data
- C. Federated learning
- D. Data Loss Prevention API
Answer: C
NEW QUESTION # 44
Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?
- A. RMSE
- B. F Score with higher precision weighting than recall
- C. F1 Score
- D. F Score with higher recall weighted than precision
Answer: D
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: D
Explanation:
Reference:
https://stackoverflow.com/questions/59394947/how-to-fix-resourceexhaustederror-oom-when-allocating-tensor/59395251#:~:text=OOM%20stands%20for%20%22out%20of,in%20your%20Dense%20%2C%20Conv2D%20layers
NEW QUESTION # 46
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- B. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
- C. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
- D. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
Answer: C
NEW QUESTION # 47
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- B. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- C. Significantly increase the max_batch_size TensorFlow Serving parameter
- D. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
Answer: A
NEW QUESTION # 48
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. Create an experiment in Kubeflow Pipelines to organize multiple runs
- B. Run multiple training jobs on Al Platform with similar job names
- C. Create multiple models using AutoML Tables
- D. Automate multiple training runs using Cloud Composer
Answer: A
Explanation:
https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/ https://www.kubeflow.org/docs/components/pipelines/concepts/run/
NEW QUESTION # 49
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?
- A. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption
- B. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
- C. Before training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.
- D. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt.
Answer: C
Explanation:
This approach would allow you to keep the critical columns of data while reducing the sensitivity of the dataset by removing the personally identifiable information (PII) before training the model. By creating an authorized view of the data, you can ensure that sensitive values cannot be accessed by unauthorized individuals.
NEW QUESTION # 50
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- B. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- C. Significantly increase the max_batch_size TensorFlow Serving parameter
- D. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
Answer: A
Explanation:
https://www.tensorflow.org/tfx/serving/performance
NEW QUESTION # 51
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