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Google Professional Machine Learning Engineer Exam is a highly sought-after certification in the field of machine learning. It is intended for professionals who have extensive experience in designing and implementing machine learning models and workflows using Google Cloud Platform technologies. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preprocessing, feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment. Passing Professional-Machine-Learning-Engineer exam demonstrates that the candidate has the skills and knowledge required to design, develop, and deploy production-grade machine learning models on Google Cloud Platform.
Google Professional Machine Learning Engineer Certification Exam consists of multiple-choice questions and performance-based scenarios that test your ability to design and implement machine learning models on the Google Cloud Platform. Professional-Machine-Learning-Engineer Exam covers a wide range of topics, including data preparation, model training and evaluation, and deployment of machine learning models in a production environment.
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The Google Professional-Machine-Learning-Engineer exam consists of multiple-choice questions and coding exercises that test a candidate's knowledge and practical skills in machine learning development, data preparation, model training, and deployment. Professional-Machine-Learning-Engineer Exam is three hours long and is held at a proctored testing center, or online for an additional fee.
Google Professional Machine Learning Engineer Sample Questions (Q64-Q69):
NEW QUESTION # 64
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?
- A. This is a good result because the accuracy across both groups is greater than 80%.
- B. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
- C. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
- D. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
Answer: C
Explanation:
This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:
It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model's accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).
It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model's accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model's accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.
Reference:
How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery
NEW QUESTION # 65
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- B. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- D. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
Answer: A
NEW QUESTION # 66
You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?
- A. Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.
- B. Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.
- C. Train an object detection model in AutoML by using the annotated image data.
- D. Train an object detection model in Vertex Al custom training by using the annotated image data.
Answer: C
NEW QUESTION # 67
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. 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.
- B. There is training-serving skew in your production environment.
- 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: A
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 # 68
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 each categorical value into an integer value.
- B. Convert each categorical value into a run-length encoded string.
- C. Map the categorical variables into a vector of boolean values.
- D. Convert the categorical string data to one-hot hash buckets.
Answer: D
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
