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Google Professional Machine Learning Engineer Sample Questions (Q93-Q98):

NEW QUESTION # 93
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

• A. There is training-serving skew in your production environment.
• B. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
• C. There is not a sufficient amount of training data.
• D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

Answer: B

Explanation:
The most likely problem is that the tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table. This is because the RAND() function generates a random number between 0 and 1 for each row, and the probability of a row being in both the training and validation tables is 0.2 * 0.8 = 0.16, which is not negligible. This means that some of the records that you use to validate your model are also used to train your model, which can lead to overfitting and poor generalization. Moreover, the probability of a row being in neither the training nor the validation table is 0.2 *
0.2 = 0.04, which means that you are wasting some of the data in your initial table and reducing the size of your datasets. A better way to split your data into training and validation sets is to use a hash function on a unique identifier column, such as the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS (SELECT * FROM
'myproject.mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) < 8); CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) >= 8); This way, you can ensure that each row has a fixed 80% chance of being in the training table and a 20% chance of being in the validation table, without any overlap or omission.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* BigQuery ML: Splitting data for training and testing
* BigQuery: FARM_FINGERPRINT function

NEW QUESTION # 94
As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

• A. Create a serving pipeline in Compute Engine for prediction
• B. Deploy the model on Al Platform and create a version of it for online inference.
• C. Use Cloud Functions for prediction each time a new data point is ingested
• D. Use the batch prediction functionality of Al Platform

Answer: D

Explanation:
Batch prediction is the process of using an ML model to make predictions on a large set of data points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can be done in batches, such as digitizing scanned customer forms at the end of each day. Batch prediction can also handle large volumes of data and scale up or down the resources as needed. AI Platform provides a batch prediction service that allows users to submit a job with their TensorFlow model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storage as well. This service requires minimal manual intervention and can be automated with Cloud Scheduler or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best option for this use case.
Reference:
Batch prediction overview
Using batch prediction

NEW QUESTION # 95
You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

• A. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
• B. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
• C. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
• D. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

Answer: C

Explanation:
* Dataflow is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.
* Al Platform is a unified platform that enables you to build and run machine learning applications across Google Cloud2.
* BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.
These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:
* DataProc is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing4.
* AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.
* Cloud Bigtable is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.
* Cloud Functions is a serverless execution environment for building and connecting cloud services.
However, it is not suitable for storing or visualizing data.
* Cloud Storage is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.

NEW QUESTION # 96
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendationmodel that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

• A. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
• B. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
• C. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
• D. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.

Answer: D

Explanation:
* Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
* Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
* Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
* Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they
* are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (suchas news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
References:
* Collaborative filtering
* Word2vec
* Cosine similarity
* Logistic regression
* SVM

NEW QUESTION # 97
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

• A. Convert the categorical string data to one-hot hash buckets.
• B. Convert each categorical value into an integer value.
• C. Convert each categorical value into a run-length encoded string.
• D. Map the categorical variables into a vector of boolean values.

Answer: A

Explanation:
Option A is incorrect because converting each categorical value into an integer value is not a good way to encode categorical values with high cardinality. This method implies an ordinal relationship between the categories, which may not be true. For example, assigning the values 1, 2, and 3 to the categories "red", "green", and "blue" does not make sense, as there is no inherent order among these colors1.
Option B is correct because converting the categorical string data to one-hot hash buckets is a suitable way to encode categorical values with high cardinality. This method uses a hash function to map each category to a fixed-length vector of binary values, where only one element is 1 and the rest are 0. This method preserves the sparsity and independence of the categories, and reduces the dimensionality of the input space2.
Option C is incorrect because mapping the categorical variables into a vector of boolean values is not a valid way to encode categorical values with high cardinality. This method implies that each category can be represented by a combination of true/false values, which may not be possible for a large number of categories. For example, if there are 10,000 categories, then there are 2

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