Question 1

You have developed a BigQuery ML model that predicts customer churn and deployed the model to Vertex Al Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

Options :

A : 1. Enable request-response logging on Vertex Al Endpoints.2 Schedule a TensorFlow Data Validation job to monitor prediction drift3. Execute model retraining if there is significant distance between the distributions

B : 1. Enable request-response logging on Vertex Al Endpoints2. Schedule a TensorFlow Data Validation job to monitor training/serving skew3. Execute model retraining if there is significant distance between the distributions

C : 1 Create a Vertex Al Model Monitoring job configured to monitor prediction drift.2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitonng alert isdetected.3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery

D : 1. Create a Vertex Al Model Monitoring job configured to monitor training/serving skew2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert isdetected3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.

Answer: C

Question 2

You work on a team in a data center that is responsible for server maintenance. Your management team wants you to build a predictive maintenance solution that uses monitoring data to detect potential server failures. Incident data has not been labeled yet. What should you do first?

Options :

A : Train a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.

B : Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Use this heuristic to monitor server performance in real time.

C : Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.

D : Hire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

Answer: D

Question 3

You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (PII) to Google Cloud. You want to use theCloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the PII is not accessible by unauthorized individuals?

Options :

A : Stream all files to Google Cloud, and then write the data to BigQuery. Periodically conduct a bulk scan of the table using the DLP API.

B : Stream all files to Google Cloud, and write batches of the data to BigQuery. While the data is being written to BigQuery, conduct a bulk scan of the data using the DLP API.

C : Create two buckets of data: Sensitive and Non-sensitive. Write all data to the Non-sensitive bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket.

D : Create three buckets of data: Quarantine, Sensitive, and Non-sensitive. Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

Answer: A

Question 4

You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

Options :

A : Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.

B : Write SQL queries to transform the data in-place in BigQuery.

C : Add the transformations as a preprocessing layer in the TensorFlow models.

D : Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.

Answer: C

Question 5

You have been given a dataset with sales predictions based on your companys marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the dat a. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

Options :

A : Use BigQuery ML to run several regression models, and analyze their performance.

B : Read the data from BigQuery using Dataproc, and run several models using SparkML

C : Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.

D : Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

Answer: A

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