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| Section | Objectives |
|---|---|
| Topic 1: Deployment and operations | - Model deployment
|
| Topic 2: Designing ML solutions | - ML architecture design
|
| Topic 3: Data preparation and processing | - Feature engineering
|
| Topic 4: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 5: ML model development | - Evaluation
|
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NEW QUESTION # 120
You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost. What hardware should you choose?
Answer: D
Explanation:
The best hardware to choose for your model while minimizing cost is a Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU. This hardware configuration can provide you with high performance, scalability, and efficiency for your TensorFlow model, as well as low cost and flexibility for your long-running and checkpointing process. The v3-8 TPU is a cloud tensor processing unit (TPU) device, which is a custom ASIC chip designed by Google to accelerate ML workloads.
It can handle large and complex models and datasets, and offer fast and stable training and inference. The n1-standard-16 is a general-purpose VM that can support the CPU and memory requirements of your model, as well as the data preprocessing and postprocessing tasks. By choosing a preemptible v3-8 TPU, you can take advantage of the lower price and availability of the TPU devices, as long as you can tolerate the possibility of the device being reclaimed by Google at any time. However, since you have built frequent checkpointing into your training process, you can resume your model from the last saved state, and avoid losing any progress or data. Moreover, you can use the Vertex AI Workbench user-managed notebooks to create and manage your notebooks instances, and leverage the integration with Vertex AI and other Google Cloud services.
The other options are not optimal for the following reasons:
* A. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs is not a good option, as it has higher cost and lower performance than the v3-8 TPU. The NVIDIA P100 GPUs are the previous generation of GPUs from NVIDIA, which have lower performance, scalability, and efficiency than the latest NVIDIA A100 GPUs or the TPUs. They also have higher price and lower availability than the preemptible TPUs, which can increase the cost and complexity of your solution.
* B. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU is not a good option, as it has higher cost and lower performance than the v3-8 TPU. It also has less GPU memory and compute power than the option with 4 NVIDIA P100 GPUs, which can limit the size and complexity of your model, and affect the training and inference speed and quality.
* C. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a
* non-preemptible v3-8 TPU is not a good option, as it has higher cost and lower flexibility than the preemptible v3-8 TPU. The non-preemptible v3-8 TPU has the same performance, scalability, and efficiency as the preemptible v3-8 TPU, but it has higher price and lower availability, as it is reserved for your exclusive use. Moreover, since your model is long-running and checkpointing, you do not need the guarantee of the device not being reclaimed by Google, and you can benefit from the lower cost and higher availability of the preemptible v3-8 TPU.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Cloud TPU
* Vertex AI Workbench user-managed notebooks
* Preemptible VMs
* NVIDIA Tesla P100 GPU
NEW QUESTION # 121
You 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?
Answer: A
Explanation:
The key is to understand the amount of data that needs to be used for training - the sensor collects tens of millions of records every day and the model needs to use all the data up to the current date.
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular- data/classification-regression/prepare-data
NEW QUESTION # 122
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?
Answer: B
Explanation:
* Option A is incorrect because using batch prediction mode instead of online mode does not solve the
"Out of Memory" error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
* Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the "Out of Memory" error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
* Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about 33%
3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
* Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the "Out of Memory" error, but rather increases the number of requests that can be sent to the prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
References:
* Choosing between online and batch prediction
* Online prediction input data
* Base64 encoding
* Preparing data for prediction
* Quotas and limits
NEW QUESTION # 123
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
Answer: B
Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
NEW QUESTION # 124
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?
Answer: B
NEW QUESTION # 125
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