Top Quality Google Professional-Machine-Learning-Engineer Sample Question Online

Exam Code: Professional-Machine-Learning-Engineer (Practice Exam Latest Test Questions VCE PDF)
Exam Name: Google Professional Machine Learning Engineer
Certification Provider: Google
Free Today! Guaranteed Training- Pass Professional-Machine-Learning-Engineer Exam.

Check Professional-Machine-Learning-Engineer free dumps before getting the full version:

NEW QUESTION 1
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

  • A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
  • B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
  • C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
  • D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositorie
  • E. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

Answer: B

NEW QUESTION 2
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. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
  • B. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
  • C. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
  • D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Answer: B

NEW QUESTION 3
You are going to train a DNN regression model with Keras APIs using this code:
Professional-Machine-Learning-Engineer dumps exhibit
How many trainable weights does your model have? (The arithmetic below is correct.)

  • A. 501*256+257*128+2 = 161154
  • B. 500*256+256*128+128*2 = 161024
  • C. 501*256+257*128+128*2=161408
  • D. 500*256*0 25+256*128*0 25+128*2 = 40448

Answer: D

NEW QUESTION 4
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. Federated learning
  • C. MD5 to encrypt data
  • D. Data Loss Prevention API

Answer: B

NEW QUESTION 5
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

  • A. Use the interleave option for reading data
  • B. Reduce the value of the repeat parameter
  • C. Increase the buffer size for the shuffle option.
  • D. Set the prefetch option equal to the training batch size
  • E. Decrease the batch size argument in your transformation

Answer: AD

NEW QUESTION 6
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 data. 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 L2 regularization and dropout parameters
  • D. 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.

Answer: A

NEW QUESTION 7
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. 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.
  • 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 thata 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 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: B

NEW QUESTION 8
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. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
  • C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
  • D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: A

NEW QUESTION 9
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. Use Al Platform for distributed training
  • B. Create a cluster on Dataproc for training
  • C. Create a Managed Instance Group with autoscaling
  • D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Answer: D

NEW QUESTION 10
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

  • A. Create a tf.data.Dataset.prefetch transformation
  • B. Convert the images to tf .Tensor Objects, and then run Datase
  • C. from_tensor_slices{).
  • D. Convert the images to tf .Tensor Objects, and then run t
  • E. dat
  • F. Datase
  • G. from_tensors ().
  • H. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the t
  • I. data API to read the images for training

Answer: D

NEW QUESTION 11
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

  • A. Build a classification model
  • B. Build a knowledge-based filtering model
  • C. Build a collaborative-based filtering model
  • D. Build a regression model using the features as predictors

Answer: C

NEW QUESTION 12
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 number of parallel trials
  • B. Decrease the range of floating-point values
  • C. Set the early stopping parameter to TRUE
  • D. Change the search algorithm from Bayesian search to random search.
  • E. Decrease the maximum number of trials during subsequent training phases.

Answer: DE

NEW QUESTION 13
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
Professional-Machine-Learning-Engineer dumps exhibit
A)
Professional-Machine-Learning-Engineer dumps exhibit
B)
Professional-Machine-Learning-Engineer dumps exhibit
C)
Professional-Machine-Learning-Engineer dumps exhibit
D)
Professional-Machine-Learning-Engineer dumps exhibit

  • A. Option A
  • B. Option B
  • C. Option C
  • D. Option D

Answer: D

NEW QUESTION 14
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 training is reproducible
  • B. Ensure that all hyperparameters are tuned
  • C. Ensure that model performance is monitored
  • D. Ensure that feature expectations are captured in the schema

Answer: B

NEW QUESTION 15
You trained a text classification model. You have the following SignatureDefs:
Professional-Machine-Learning-Engineer dumps exhibit
What is the correct way to write the predict request?

  • A. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})
  • B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
  • C. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
  • D. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

Answer: B

NEW QUESTION 16
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. • 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
  • B. • 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
  • C. • 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.
  • D. • 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

Answer: D

NEW QUESTION 17
......

100% Valid and Newest Version Professional-Machine-Learning-Engineer Questions & Answers shared by Surepassexam, Get Full Dumps HERE: https://www.surepassexam.com/Professional-Machine-Learning-Engineer-exam-dumps.html (New 60 Q&As)