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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing

The following will be discussed in Google Professional-Machine-Learning-Engineer Exam Dumps:

Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
Feature selection
Handling missing data
Managing large samples (TFRecords)
Design data pipelines
Class imbalance
Feature engineering
Monitoring/changing deployed pipelines
Statistical fundamentals at scale
Feature crosses
Handling outliers
Data ingestion
Transformations (TensorFlow Transform)
Data exploration (EDA)
Data validation
Evaluation of data quality and feasibility
Data leakage and augmentation
Data privacy and compliance
Encoding structured data types
Database migration
Build data pipelines
Streaming data (e.g. from IoT devices)
Visualization

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Google Professional Machine Learning Engineer is a certification exam offered by Google Cloud. It is designed to test the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam is intended for individuals who have experience in machine learning and wish to demonstrate their proficiency in designing and implementing machine learning models using Google Cloud technologies.

Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer Exam Dumps Pdf will include the following topics:

Data Preparation and Processing
ML Solution Architecture
ML Model Development

Google Professional Machine Learning Engineer Sample Questions (Q277-Q282):

NEW QUESTION # 277
You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

A. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.
B. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.
C. Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.
D. Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

Answer: C

Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a managed service for training custom models with various frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost. To train your PyTorch model with Vertex AI, you need to package your code with Setuptools, which is a Python tool for creating and distributing packages. You also need to use a pre-built container, which is a Docker image that contains the dependencies and libraries for your framework.
You can choose from a list of pre-built containers provided by Google, or create your own custom container.
By using a pre-built container, you can avoid the hassle of installing and configuring the environment for your model. You can also specify a custom tier for your training job, which allows you to select the number and type of GPUs you want to use. You can choose from various GPU options, such as V100, P100, K80, and T4.
By using 4 V100 GPUs, you can leverage the high performance and memory capacity of these accelerators to train your model faster and cheaper than using CPUs. This solution requires minimal changes to your code and can scale your training workload efficiently. References:
* Vertex AI | Google Cloud
* Custom training with pre-built containers | Vertex AI
* [Using GPUs | Vertex AI]

NEW QUESTION # 278
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

A. Compare the loss performance for each model on the validation data
B. Compare the loss performance for each model on a held-out dataset.
C. Compare the mean average precision across the models using the Continuous Evaluation feature
D. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

Answer: A

NEW QUESTION # 279
You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3 Obtain metrics for the evaluation dataset.
4 Compare models trained in different pipeline executions
How should you execute these steps'?

A. 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits.
2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table
B. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2 Enable autologging of metrics in the training component
3 Compare models using the artifacts lineage in Vertex ML Metadata
C. 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits.
2 Use a SQL view to apply feature engineering and train the model using the data in that view
3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.
D. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2. Enable auto logging of metrics in the training component.
3 Compare pipeline runs in Vertex Al Experiments

Answer: B

Explanation:
' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.
Explanation:
Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud. You can use Vertex AI Pipelines to add a component to divide the data into training and evaluation sets, and add another component for feature engineering. A component is a self-contained piece of code that performs a specific task in the pipeline. You can use the built-in components provided by Vertex AI Pipelines, or create your own custom components. By using Vertex AI Pipelines, you can orchestrate and automate your ML workflow, and track the provenance and lineage of your data and models. You can also enable autologging of metrics in the training component, which is a feature that automatically logs the metrics from your XGBoost model to Vertex AI Experiments. Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to monitor the training progress, visualize the metrics, and analyze the results of your model. You can also compare models using the artifacts lineage in Vertex ML Metadata. Vertex ML Metadata is a service that stores and manages the metadata of your ML artifacts, such as datasets, models, metrics, and executions. You can use Vertex ML Metadata to view the artifacts lineage, which is a graph that shows the relationships and dependencies among the artifacts. By using the artifacts lineage, you can compare the performance and quality of different models trained in different pipeline executions, and identify the best model for your use case. By using Vertex AI Pipelines, Vertex AI Experiments, and Vertex ML Metadata, you can execute the steps required for developing a training pipeline for a new XGBoost classification model based on tabular data stored in a BigQuery table. Reference:
Vertex AI Pipelines documentation
Vertex AI Experiments documentation
Vertex ML Metadata documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 280
You are training a TensorFlow model on a structured dataset with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage.
B. Load the data into Cloud Bigtable, and read the data from Bigtable.
C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS).
D. Load the data into BigQuery, and read the data from BigQuery.

Answer: A

Explanation:
Cloud Bigtable is typically used to process unstructured data, such as time-series data, logs, or other types of data that do not conform to a fixed schema. However, Cloud Bigtable can also be used to store structured data if necessary, such as in the case of a key-value store or a database that does not require complex relational queries.

NEW QUESTION # 281
You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

A. Store image files in Cloud Storage and access them by using serialized records.
B. Store image files in Cloud Storage and access them directly.
C. Store image files in Cloud Filestore and access them directly by using an NFS mount point.
D. Store image files in Cloud Filestore, and access them by using serialized records.

Answer: A

Explanation:
Cloud Storage is a scalable and cost-effective storage service for any type of data. By storing image files in Cloud Storage, you can access them from anywhere and avoid the overhead of managing your own storage infrastructure. However, accessing image files directly from Cloud Storage can be slow and inefficient, especially for large-scale training. A better option is to use serialized records, such as TFRecord or Apache Avro, which are binary formats that store multiple images and their labels in a single file. Serialized records can improve the data throughput and reduce the network latency, as well as enable data compression and sharding. You can use TensorFlow or Apache Beam APIs to create and read serialized records from Cloud Storage. This solution requires minimal code changes and can speed up your training time significantly. Reference:
Cloud Storage | Google Cloud
TFRecord and tf.Example | TensorFlow Core
Apache Avro 1.10.2 Specification
Using Apache Beam with Cloud Storage | Cloud Storage

NEW QUESTION # 282
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