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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Topic 1
  • Defining the input (features) and predicted output format
  • Modeling techniques given interpretability requirements
Topic 2
  • Training a model as a job in different environments
  • Constructing and testing of parameterized pipeline definition in SDK
Topic 3
  • Choose appropriate Google Cloud software components
  • Assessing and communicating business impact
Topic 4
  • Optimization and simplification of input pipeline for training
  • Aligning with Google AI principles and practices
Topic 5
  • Design architecture that complies with regulatory and security concerns
  • Define business success criteria
Topic 6
  • Choose appropriate Google Cloud hardware components
  • Privacy implications of data usage
  • Identifying potential regulatory issues
Topic 7
  • Performance and business quality of ML model predictions
  • Establishing continuous evaluation metrics
Topic 8
  • Organization and tracking experiments and pipeline runs
  • Hooking models into existing CI
  • CD deployment system
Topic 9
  • Model performance against baselines, simpler models, and across the time dimension
  • Defining outcome of model predictions
Topic 10
  • Automation of data preparation and model training
  • deployment
  • Determination of when a model is deemed unsuccessful

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

  • Hardware accelerators
  • Transfer learning
  • Overfitting
  • Model generalization
  • Build a model
  • Training a model as a job in different environments
  • Model performance against baselines, simpler models, and across the time dimension
  • Choice of framework and model
  • Scale model training and serving
  • Model explainability on Cloud AI Platform
  • Modeling techniques given interpretability requirements
  • Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
  • Retraining/redeployment evaluation
  • Unit tests for model training and serving
  • Productionizing
  • Distributed training

Google Professional Machine Learning Engineer Sample Questions (Q67-Q72):

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the dat a. You want to make your model training pipeline more robust to issues like this. What should you do?

  • A. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
  • B. Use TensorFlow Data Validation to detect and flag schema anomalies.
  • C. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
  • D. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.

Answer: D

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 transformation
  • B. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
  • C. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
  • D. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).

Answer: B

Cite from Google Pag: to construct a Dataset from data in memory, use or When input data is stored in a file (not in memory), the recommended TFRecord format, you can use is for data in memory. is for data in non-memory storage.
" Store image, video, audio and unstructured data on Cloud Storage Store these data in large container formats on Cloud Storage. This applies to sharded TFRecord files if you're using TensorFlow, or Avro files if you're using any other framework. Combine many individual images, videos, or audio clips into large files, as this will improve your read and write throughput to Cloud Storage. Aim for files of at least 100mb, and between 100 and 10,000 shards. To enable data management, use Cloud Storage buckets and directories to group the shards. "

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 Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
  • B. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
  • C. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
  • D. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API

Answer: B

Explanation: Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI."

You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost. What should you do?

  • A. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.
  • B. Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.
  • C. Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.
  • D. Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.

Answer: A

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

Answer: B,D


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