Professional Machine Learning Engineer - Google Cloud Certified Exam Questions

A- A. Shuffle the data before training, and iteratively adjust the batch size until the loss improves.

B- B. Explore the feature set to remove NaNs and clip any noisy outliers. Shuffle the data before retraining.

C- C. Switch from L1 to L2 regularization, and iteratively adjust the L2 penalty until the loss improves.

D- D. Adjust the learning rate to exponentially decay with a larger decrease at the step where the loss jumped, and iteratively adjust the initial learning rate until the loss improves.

A- A. Use the batch prediction functionality of VertexAI

B- B. Create a serving pipeline in Compute Engine for prediction.

C- C. Use Cloud Functions for prediction each time a new data point is ingested.

D- D. Deploy the model on Vertex AI and create a version of it for online inference.

A- C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage.

B- B. Load the data into Cloud Bigtable, and read the data from Bigtable.

C- D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS).

D- A. Load the data into BigQuery, and read the data from BigQuery.

A- A. Use the Vertex AI Training built-in algorithms to create a custom model.

B- B. Use AutoML Natural Language to extract custom entities for classification.

C- C. Use the Cloud Natural Language API to extract custom entities for classification.

D- D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

A- A. Thee individual features: binned latitude, binned longitude, and one-hot encoded car type.

B- B. One feature obtained as an element-wise product between latitude, longitude, and car type.

C- C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

D- D. Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

A- A. 1. Create a Pub/Sub topic for each user. 2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

B- B. 1. Create a Pub/Sub topic for each user. 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

C- C. 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.

D- D. 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

A- A. Keep the original feature set, and add L1 regularization to the loss function.

B- B. Use principal component analysis (PCA), and select the first n components that explain 99% of the variance.

C- C. Perform correlation analysis. Remove features that are highly correlated to one another and features that are not correlated to the target.

D- D. Ensure that categorical features are one-hot encoded and that continuous variables are binned, and create feature crosses for a subset of relevant features.

A- A. Use TensorFlow Data Validation to detect and flag schema anomalies.

B- B. 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.

C- C. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

D- D. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

A- Distribute texts randomly across the train-test-eval subsets: Train set: [TextA1, TextB2, ...] Test set: [TextA2, TextC1, TextD2, ...] Eval set: [TextB1, TextC2, TextD1, ...]

B- Distribute authors randomly across the train-test-eval subsets: (*) Train set: [TextA1, TextA2, TextD1, TextD2, ...] Test set: [TextB1, TextB2, ...] Eval set: [TexC1,TextC2 ...]

C- Distribute sentences randomly across the train-test-eval subsets: Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 ...] Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 ...] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 ...]

D- Distribute paragraphs of texts (i.e., chunks of consecutive sentences) across the train-test-eval subsets: Train set: [SentenceA11, SentenceA12, SentenceD11, SentenceD12 ...] Test set: [SentenceA13, SentenceB13, SentenceB21, SentenceD23, SentenceC12, SentenceD13 ...] Eval set: [SentenceA11, SentenceA22, SentenceB13, SentenceD22, SentenceC23, SentenceD11 ...]

A- Use the Natural Language API to classify support requests.

B- Use AutoML Natural Language to build the support requests classifier.

C- Use an established text classification model on Vertex AI to perform transfer learning.

D- Use an established text classification model on Vertex AI as-is to classify support requests.