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Google Professional Machine Learning Engineer certification is a valuable asset for professionals who are looking to advance their careers in the field of machine learning. Google Professional Machine Learning Engineer certification validates the candidate's expertise in designing, building, and deploying machine learning models using the Google Cloud Platform. If you are a data scientist, machine learning engineer, or software developer looking to enhance your skills in machine learning, then the Google Professional Machine Learning Engineer certification is definitely worth considering.
NEW QUESTION # 107
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. Increase the buffer size for the shuffle option.
- B. Reduce the value of the repeat parameter
- C. Decrease the batch size argument in your transformation
- D. Set the prefetch option equal to the training batch size
- E. Use the interleave option for reading data
Answer: C,E
NEW QUESTION # 108
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. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
- 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. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
Answer: C
NEW QUESTION # 109
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. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- B. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- 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. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
Answer: A
Explanation:
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture
NEW QUESTION # 110
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. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
- B. Perform feature selection on the model, and retrain the model with fewer features
- C. Create alerts to monitor for skew, and retrain the model.
- D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
Answer: A
NEW QUESTION # 111
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:
You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?
- A.
- B.
- C.
- D.
Answer: B
Explanation:
If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use
NEW QUESTION # 112
Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?
- A. 1 Iterate over your local Tiles in Python
2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data
3. Call the speech: recognize API endpoint to generate transcriptions
4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions - B. 1 Iterate over your local files in Python
2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data
3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions - C. 1 Upload the audio files to Cloud Storage
2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions
3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions - D. 1 Upload the audio files to Cloud Storage
2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.
3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method
Answer: D
Explanation:
4 Call the Natural Language API by using the analyzesenriment method
NEW QUESTION # 113
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 users' IAM policy to allow access to Amazon SageMaker Service API calls only.
- B. Add a SageMaker Runtime VPC endpoint interface to the VPC.
- C. Modify the security group on the endpoint network interface to restrict access to the instances.
- D. Add a VPC endpoint policy to allow access to the IAM users.
- E. Modify the ACL on the endpoint network interface to restrict access to the instances.
Answer: C,D
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/
NEW QUESTION # 114
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A. Recall
- B. Misclassification rate
- C. Mean absolute percentage error (MAPE)
- D. Area Under the ROC Curve (AUC)
Answer: D
NEW QUESTION # 115
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. 500*256*0 25+256*128*0 25+128*2 = 40448
- B. 500*256+256*128+128*2 = 161024
- C. 501*256+257*128+2 = 161154
- D. 501*256+257*128+128*2=161408
Answer: A
NEW QUESTION # 116
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?
- A. Store features in Vertex Al Feature Store.
- B. Store features in Bigtable as key/value data.
- C. Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
- D. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.
Answer: A
NEW QUESTION # 117
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?
- A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
- B. Extend your test dataset with images of the newer products when they are introduced to retraining
- C. Replace your test dataset with images of the newer products when they are introduced to retraining.
- D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
Answer: B
NEW QUESTION # 118
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Create a FindMatches machine learning transform in AWS Glue.
- B. Search for duplicate accounts in the AWS Glue Data Catalog.
- C. Execute the built-in FindDuplicates Amazon Athena query.
- D. Create an AWS Glue crawler to infer duplicate accounts in the source data.
Answer: A
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 119
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 "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- D. Use the "Other Products You May Like" recommendation type to increase the click-through rate
Answer: B
NEW QUESTION # 120
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?
- A. Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
- B. Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.
- C. Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
- D. Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
Answer: D
NEW QUESTION # 121
You recently trained an XGBoost model on tabular data You plan to expose the model for internal use as an HTTP microservice After deployment you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?
- A. Deploy the model to BigQuery ML by using CREATE model with the BOOSTED-THREE-REGRESSOR statement and invoke the BigQuery API from the microservice.
- B. Build a Flask-based app Package the app in a custom container on Vertex Al and deploy it to Vertex Al Endpoints.
- C. Use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex Al Endpoints.
- D. Build a Flask-based app Package the app in a Docker image and deploy it to Google Kubernetes Engine in Autopilot mode.
Answer: C
NEW QUESTION # 122
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?
- A. Create a hot-encoding of words, and feed the encodings into your model.
- B. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
- C. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
- D. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
Answer: D
NEW QUESTION # 123
You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?
- A. Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.
- B. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
- C. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
- D. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
Answer: A
NEW QUESTION # 124
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
- A. Linear regression
- B. K-means
- C. Principal component analysis (PCA)
- D. Logistic regression
Answer: A
NEW QUESTION # 125
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 dropout rate for the second-to-last layer.
- D. Increase the number of epochs for model training
Answer: D
NEW QUESTION # 126
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
- A. Dimensionality reduction
- B. Weight pruning
- C. Dynamic range quantization
- D. Model distillation
Answer: D
NEW QUESTION # 127
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 dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. 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.
- B. Apply a dropout parameter of 0 2, 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. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
Answer: A
NEW QUESTION # 128
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. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- B. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- 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. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
Answer: A
NEW QUESTION # 129
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