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NEW QUESTION # 183
You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?
Answer: D
Explanation:
* Option A is not the best answer because it requires using Apache Spark and Dataproc, which may incur additional cost and complexity for running and managing the cluster. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
* Option B is not the best answer because it requires loading the data into a pandas DataFrame, which may not be scalable or efficient for large datasets. It also requires training the model directly on the DataFrame, which may not leverage the distributed computing capabilities of BigQuery.
* Option C is the best answer because it allows performing preprocessing in BigQuery by using SQL, which is a cost-effective and efficient way to manipulate large datasets. It also allows using the BigQueryClient in TensorFlow to read the data directly from BigQuery, which is a convenient and fast way to access the data for training the model1.
* Option D is not the best answer because it requires using Apache Beam and Dataflow, which may incur additional cost and complexity for running and managing the pipeline. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
References:
* 1: Read data from BigQuery | TensorFlow I/O
NEW QUESTION # 184
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?
Answer: C
Explanation:
Recommendations AI is a service that allows users to build, test, and deploy personalized product recommendations for their ecommerce websites. It uses Google's deep learning models to learn from user behavior and product data, and generate high-quality recommendations that can increase revenue, click-through rate, and customer satisfaction. One of the best practices for using Recommendations AI is to choose the right recommendation type for the business objective. The "Frequently Bought Together" recommendation type shows products that are often purchased together with the current product, and encourages users to add more items to their shopping cart. This can increase the average order value and the revenue for each transaction. The other options are not as effective or feasible for this objective. The "Other Products You May Like" recommendation type shows products that are similar to the current product, and may increase the click-through rate, but not necessarily the shopping cart size. Importing the user events and then the product catalog is not a recommended order, as it may cause data inconsistency and missing recommendations. The product catalog should be imported first, and then the user events. Using placeholder values for the product catalog is not a viable option, as it will not produce meaningful recommendations or reflect the real performance of the model. Reference:
Recommendations AI documentation
Choosing a recommendation type
Importing data to Recommendations AI
NEW QUESTION # 185
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?
Answer: A
NEW QUESTION # 186
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?
Answer: C
Explanation:
The best hardware to choose for your models is a cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM. This hardware configuration can provide you with enough compute power, memory, and bandwidth to handle your large and complex deep learning models, as well as your custom TensorFlow ops in C++. The NVIDIA Tesla A100 GPUs are the latest and most advanced GPUs from NVIDIA, which offer high performance, scalability, and efficiency for various ML workloads. They also support multi-instance GPU (MIG) technology, which allows you to partition each GPU into up to seven smaller instances, each with its own memory, cache, and compute cores. This can enable you to run multiple experiments in parallel, or to optimize the resource utilization and cost efficiency of your models. The a2-megagpu-16g machines are part of the Google Cloud Accelerator- Optimized VM (A2) family, which are designed to provide the best performance and flexibility for GPU- intensive applications. They also offer high-speed NVLink interconnects between the GPUs, which can improve the data transfer and communication between the GPUs. Moreover, the a2-megagpu-16g machines have 96 vCPUs and 1.4 TB RAM, which can support the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
The other options are not optimal for the following reasons:
* A. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The NVIDIA Tesla V100 GPUs are the previous generation of GPUs from NVIDIA, which have lower performance, scalability, and efficiency than the NVIDIA Tesla A100 GPUs. They also do not support the MIG technology, which can limit the flexibility and optimization of your models. Moreover, the n1- highcpu-64 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
* C. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM is not a good option, as it has less GPU memory, compute power, and bandwidth than the a2-megagpu-16g machines. The v2-8 TPU is a cloud tensor processing unit (TPU) device, which is a custom ASIC chip designed by Google to accelerate ML workloads. However, the v2-8 TPU is the second generation of TPUs, which have lower performance, scalability, and efficiency than the latest v3-8 TPUs. They also have less memory and bandwidth than the NVIDIA Tesla A100 GPUs, which can limit the size and complexity of your models, as well as the data transfer and communication between the devices. Moreover, the n1-highcpu-
64 machine has lower vCPUs and RAM than the a2-megagpu-16g machines, which can affect the CPU and memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
* D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM is not a good option, as it does not have any GPUs, which are essential for accelerating deep learning models. The n1- highcpu-96 machines are part of the Google Cloud N1 VM family, which are general-purpose VMs that do not offer the best performance and features for GPU-intensive applications. They also have lower RAM than the a2-megagpu-16g machines, which can affect the memory requirements of your models, as well as the data preprocessing and postprocessing tasks.
:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification NVIDIA Tesla A100 GPU Google Cloud Accelerator-Optimized VM (A2) family Google Cloud N1 VM family Cloud TPU
NEW QUESTION # 187
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
Answer: D
Explanation:
The best option for creating and orchestrating an end-to-end training pipeline for a TensorFlow model is to use TensorFlow Extended (TFX) and standard TFX components, and deploy the pipeline to Vertex AI Pipelines. TFX is an end-to-end platform for deploying production ML pipelines, which consists of several built-in components that cover the entire ML lifecycle, from data ingestion and validation, to model training and evaluation, to model deployment and monitoring. TFX also supports custom components and integrations with other Google Cloud services, such as BigQuery, Dataflow, and Cloud Storage. Vertex AI Pipelines is a fully managed service that allows you to run TFX pipelines on Google Cloud, without having to worry about infrastructure provisioning, scaling, or maintenance. Vertex AI Pipelines also provides a user-friendly interface to monitor and manage your pipelines, as well as tools to track and compare experiments. The other options are not as suitable for creating and orchestrating an end-to-end training pipeline for a TensorFlow model, because:
* Creating the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components would require more development time and effort, as Kubeflow Pipelines DSL is not as expressive or compatible with TensorFlow as TFX. Predefined Google Cloud components might not cover all the stages of the ML lifecycle, and might not be optimized for TensorFlow models.
* Orchestrating the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine would require more infrastructure maintenance, as Kubeflow Pipelines is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as
* you would have to pay for the cluster resources, regardless of the pipeline usage. References:
* TFX | ML Production Pipelines | TensorFlow
* Vertex AI Pipelines | Google Cloud
* Kubeflow Pipelines | Google Cloud
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
NEW QUESTION # 188
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