You work for a bank. You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex AI services to include in the workflow. You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex AI services should you use?
A. Vertex ML Metadata, Vertex AI Feature Store, and Vertex AI VizierYou are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
A. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.You recently created a new Google Cloud project. After testing that you can submit a Vertex AI Pipeline job from the Cloud Shell, you want to use a Vertex AI Workbench user-managed notebook instance to run your code from that instance. You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?
A. Ensure that the Workbench instance that you created is in the same region of the Vertex AI Pipelines resources you will use.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 Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex Al API.You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
A. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_datasetYour 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. Upload the audio files to Cloud Storage 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per hour than expected throughout the day. You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency. What should you do?
A. Deploy two models to the same endpoint, and distribute requests among them evenlyYou are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (PII) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the PII is not accessible by unauthorized individuals?
A. Stream all files to Google Cloud, and then write the data to BigQuery. Periodically conduct a bulk scan of the table using the DLP API.You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
A. Train a TensorFlow model on Vertex AI.You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?
A. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.Nowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Google exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam preparations and Google certification application, do not hesitate to visit our Vcedump.com to find your solutions here.