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Google Professional Machine Learning Engineer Sample Questions (Q161-Q166):

NEW QUESTION # 161
You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

• A. 1 Specify sampled Shapley as the explanation method with a path count of 5.
  2 Deploy the model to Vertex Al Endpoints.
  3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
• B. 1. Specify sampled Shapley as the explanation method with a path count of 50.
  2. Deploy the model to Vertex Al Endpoints.
  3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
• C. 1 Specify Integrated Gradients as the explanation method with a path count of 50.
  2. Deploy the model to Vertex Al Endpoints.
  3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
• D. 1 Specify Integrated Gradients as the explanation method with a path count of 5.
  2 Deploy the model to Vertex Al Endpoints.
  3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

Answer: A

NEW QUESTION # 162
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on AI Platform for high-throughput online prediction. Which architecture should you use?

• A. Send incoming prediction requests to a Pub/Sub topic.
  Transform the incoming data using a Dataflow job.
  Submit a prediction request to AI Platform using the transformed data.
  Write the predictions to an outbound Pub/Sub queue.
• B. Send incoming prediction requests to a Pub/Sub topic.
  Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
  Implement your preprocessing logic in the Cloud Function.
  Submit a prediction request to AI Platform using the transformed data.
  Write the predictions to an outbound Pub/Sub queue.
• C. Validate the accuracy of the model that you trained on preprocessed data.
  Create a new model that uses the raw data and is available in real time.
  Deploy the new model onto AI Platform for online prediction.
• D. Stream incoming prediction request data into Cloud Spanner.
  Create a view to abstract your preprocessing logic.
  Query the view every second for new records.
  Submit a prediction request to AI Platform using the transformed data.
  Write the predictions to an outbound Pub/Sub queue.

Answer: B

Explanation:
https://cloud.google.com/pubsub/docs/publisher

NEW QUESTION # 163
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

• A. Load the data into Cloud Bigtable, and read the data from Bigtable
• B. Load the data into BigQuery and read the data from BigQuery.
• C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
• D. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Answer: D

Explanation:
The input/output execution performance of a TensorFlow model depends on how efficiently the model can read and process the data from the data source. Reading and processing data from CSV files can be slow and inefficient, especially if the data is large and distributed. Therefore, to improve the input/output execution performance, one should use a more suitable data format and storage system.
One of the best options for improving the input/output execution performance is to convert the CSV files into shards of TFRecords, and store the data in Cloud Storage. TFRecord is a binary data format that can store a sequence of serialized TensorFlow examples. TFRecord has several advantages over CSV, such as:
* Faster data loading: TFRecord can be read and processed faster than CSV, as it avoids the overhead of parsing and decoding the text data. TFRecord also supports compression and checksums, which can
* reduce the data size and ensure data integrity1
* Better performance: TFRecord can improve the performance of the model, as it allows the model to access the data in a sequential and streaming manner, and leverage the tf.data API to build efficient data pipelines. TFRecord also supports sharding and interleaving, which can increase the parallelism and throughput of the data processing2
* Easier integration: TFRecord can integrate seamlessly with TensorFlow, as it is the native data format for TensorFlow. TFRecord also supports various types of data, such as images, text, audio, and video, and can store the data schema and metadata along with the data3 Cloud Storage is a scalable and reliable object storage service that can store any amount of data. Cloud Storage has several advantages over other storage systems, such as:
* High availability: Cloud Storage can provide high availability and durability for the data, as it replicates the data across multiple regions and zones, and supports versioning and lifecycle management. Cloud Storage also offers various storage classes, such as Standard, Nearline, Coldline, and Archive, to meet different performance and cost requirements4
* Low latency: Cloud Storage can provide low latency and high bandwidth for the data, as it supports HTTP and HTTPS protocols, and integrates with other Google Cloud services, such as AI Platform, Dataflow, and BigQuery. Cloud Storage also supports resumable uploads and downloads, and parallel composite uploads, which can improve the data transfer speed and reliability5
* Easy access: Cloud Storage can provide easy access and management for the data, as it supports various tools and libraries, such as gsutil, Cloud Console, and Cloud Storage Client Libraries. Cloud Storage also supports fine-grained access control and encryption, which can ensure the data security and privacy.
The other options are not as effective or feasible. Loading the data into BigQuery and reading the data from BigQuery is not recommended, as BigQuery is mainly designed for analytical queries on large-scale data, and does not support streaming or real-time data processing. Loading the data into Cloud Bigtable and reading the data from Bigtable is not ideal, as Cloud Bigtable is mainly designed for low-latency and high-throughput key-value operations on sparse and wide tables, and does not support complex data types or schemas.
Converting the CSV files into shards of TFRecords and storing the data in the Hadoop Distributed File System (HDFS) is not optimal, as HDFS is not natively supported by TensorFlow, and requires additional configuration and dependencies, such as Hadoop, Spark, or Beam.
References: 1: TFRecord and tf.Example 2: Better performance with the tf.data API 3: TensorFlow Data Validation 4: Cloud Storage overview 5: Performance : [How-to guides]

NEW QUESTION # 164
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

• A. Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.
• B. Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
• C. Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
• D. Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.

Answer: B

Explanation:
The best approach to build a model that predicts how much inventory the logistics team should order each month is to use a time series forecasting model to predict each item's monthly sales. This approach can capture the temporal patterns and trends in the sales data, such as seasonality, cyclicality, and autocorrelation. It can also account for the variability and uncertainty in the demand, and provide confidence intervals and error metrics for the predictions. By using a time series forecasting model, you can provide the logistics team with accurate and reliable estimates of the future sales for each item, which can help them optimize the inventory levels and avoid overstocking or understocking. You can use various methods and tools to build a time series forecasting model, such as ARIMA, LSTM, Prophet, or BigQuery ML.
The other options are not optimal for the following reasons:
* A. Using a clustering algorithm to group popular items together is not a good approach, as it does not provide any quantitative or temporal information about the sales or the inventory. It only provides a qualitative and static categorization of the items based on their similarity or dissimilarity. Moreover,
* clustering is an unsupervised learning technique, which does not use any target variable or feedback to guide the learning process. This can result in arbitrary and inconsistent clusters, which may not reflect the true demand or preferences of the customers.
* B. Using a regression model to predict how much additional inventory should be purchased each month is not a good approach, as it does not account for the individual differences and dynamics of each item.
It only provides a single aggregated value for the whole inventory, which can be misleading and inaccurate. Moreover, a regression model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A regression model can also suffer from overfitting or underfitting, depending on the choice and complexity of the features and the model.
* D. Using a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED is not a good approach, as it does not provide any numerical or predictive information about the sales or the inventory. It only provides a discrete and subjective label for the inventory levels, which can be vague and ambiguous. Moreover, a classification model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A classification model can also suffer from class imbalance, misclassification, or overfitting, depending on the choice and complexity of the features, the model, and the threshold.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Time Series Forecasting: Principles and Practice
* BigQuery ML: Time series analysis

NEW QUESTION # 165
You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

• A.
• B.
• C.
• D.

Answer: D

Explanation:
The option A is the most suitable solution for logging data and tracking artifacts from each run of a model development experiment in a Vertex AI Workbench notebook. Vertex AI Workbench is a service that allows you to create and run interactive notebooks on Google Cloud. You can use Vertex AI Workbench to experiment with different preprocessing and modeling approaches for your time series prediction problem.
You can also use the Vertex AI TensorBoard instance and the Vertex AI SDK to create an experiment and associate the TensorBoard instance. TensorBoard is a tool that allows you to visualize and monitor the metrics and artifacts of your ML experiments. You can use the Vertex AI SDK to create an experiment object, which is a logical grouping of runs that share a common objective. You can also use the Vertex AI SDK to associate the experiment object with a TensorBoard instance, which is a managed service that hosts a TensorBoard web app. By using the Vertex AI TensorBoard instance and the Vertex AI SDK, you can easily set up and manage your experiments, and access the TensorBoard web app from the Vertex AI console. You can also use the log_time_series_metrics function and the log_metrics function to log data and track artifacts from each run.
The log_time_series_metrics function is a function that allows you to log the time series data, such as the multivariate time series and the labels, to the TensorBoard instance. The log_metrics function is a function that allows you to log the scalar metrics, such as the loss values, to the TensorBoard instance. By using these functions, you can record the data and artifacts from each run of your experiment, and compare them in the TensorBoard web app. You can also use the TensorBoard web app to visualize the data and artifacts, such as the time series plots, the scalar charts, the histograms, and the distributions. By using the Vertex AI TensorBoard instance, the Vertex AI SDK, and the log functions, you can log data and track artifacts from each run of your experiment in a Vertex AI Workbench notebook. References:
* Vertex AI Workbench documentation
* Vertex AI TensorBoard documentation
* Vertex AI SDK documentation
* log_time_series_metrics function documentation
* log_metrics function documentation
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

NEW QUESTION # 166
......

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