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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)
Data validation
Data ingestion
Managing large samples (TFRecords)
Batching and streaming data pipelines at scale
Monitoring/changing deployed pipelines
Data privacy and compliance
Feature crosses
Class imbalance
Statistical fundamentals at scale
Feature selection
Encoding structured data types
Evaluation of data quality and feasibility
Streaming data (e.g. from IoT devices)
Transformations (TensorFlow Transform)
Handling outliers
Design data pipelines
Data leakage and augmentation
Database migration
Feature engineering

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Google Professional Machine Learning Engineer Training Pdf Material & Professional-Machine-Learning-Engineer Reliable Practice Questions & Google Professional Machine Learning Engineer Exam Prep Practice

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

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

Design pipeline. Considerations include:

Hybrid or multi-cloud strategies
Hooking into model and dataset versioning
Decoupling components with Cloud Build
Implement serving pipeline
Testing for target performance
Identification of components, parameters, triggers, and compute needs
Model binary options
A/B and canary testing
Google Cloud serving options
Storing data and generated artifacts
Model/dataset lineage

Google Professional Machine Learning Engineer Sample Questions (Q206-Q211):

NEW QUESTION # 206
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?

A. Oversample the fraudulent transaction 10 times.
B. Use one-hot encoding on all categorical features.
C. Write your data in TFRecords.
D. Z-normalize all the numeric features.

Answer: A

NEW QUESTION # 207
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to Apache Parquet format.
B. Convert the records to XML format.
C. Convert the records to GZIP CSV format.
D. Convert the records to JSON format.

Answer: A

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

NEW QUESTION # 208
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

A. Use the AI Explanations feature on AI Platform. Submit each prediction request with the 'explain' keyword to retrieve feature attributions using the sampled Shapley method.
B. Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
C. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
D. Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.

Answer: C

NEW QUESTION # 209
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product?

A. Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.
B. Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.
C. Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
D. Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

Answer: C

Explanation:
The best option for building a recommendation system without any user event data is to use simple heuristics based on content metadata. This is a type of content-based filtering, which recommends items that are similar to the ones that the user has interacted with or selected, based on their attributes. For example, if a user selects a comedy movie from the US released in 2020, the system can recommend other comedy movies from the US released in 2020 or nearby years. This approach does not require any machine learning, but it can leverage the existing metadata of the videos to provide relevant recommendations. It also allows the system to start collecting user event data, such as views, likes, ratings, etc., which can be used to train a more sophisticated machine learning model in the future, such as a collaborative filtering model or a hybrid model that combines content and collaborative information. Reference:
Recommendation Systems
Content-Based Filtering
Collaborative Filtering
Hybrid Recommender Systems: A Systematic Literature Review

NEW QUESTION # 210
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

A. Standardize the data by transforming it with a logarithmic function.
B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
C. Normalize the data by scaling it to have values between 0 and 1.
D. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.

Answer: C

Explanation:
The best option to ensure that the features with the largest magnitude don't overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons:
A . Standardizing the data by transforming it with a logarithmic function is not a good option, as it can distort the distribution and relationship of the data, and introduce bias and errors. Moreover, the logarithmic function is not defined for negative or zero values, which can limit its applicability and cause problems for the model.
B . Applying a principal component analysis (PCA) to minimize the effect of any particular feature is not a good option, as it can reduce the interpretability and explainability of the data and the model. PCA is a dimensionality reduction technique that transforms the data into a new set of orthogonal features that capture the most variance in the data. However, these new features are not directly related to the original features, and can lose some information and meaning in the process. Moreover, PCA can be computationally expensive and complex, and may not be necessary for the problem at hand.
C . Using a binning strategy to replace the magnitude of each feature with the appropriate bin number is not a good option, as it can lose the granularity and precision of the data, and introduce noise and outliers. Binning is a discretization technique that groups the continuous values of a feature into a finite number of bins or categories. However, this can reduce the variability and diversity of the data, and create artificial boundaries and gaps that may not reflect the true nature of the data. Moreover, binning can be arbitrary and subjective, and depend on the choice of the bin size and number.
Reference:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization sklearn.preprocessing.MinMaxScaler documentation Principal Component Analysis Explained Visually Binning Data in Python

NEW QUESTION # 211
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