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Google Professional Machine Learning Engineer Sample Questions (Q57-Q62):

NEW QUESTION # 57
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 BigQuery and read the data from BigQuery.
B. Load the data into Cloud Bigtable, and read the data from Bigtable
C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

Answer: C

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 # 58
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

A. 1 = Dataflow, 2 = BigQuery
B. 1 = Pub/Sub, 2 = Datastore
C. 1 = Cloud Function, 2 = Cloud SQL
D. 1 = Dataflow, 2 = Cloud SQL

Answer: A

Explanation:
https://github.com/GoogleCloudPlatform/dataflow-contact-center-speech-analysis

NEW QUESTION # 59
You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?

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. Test this heuristic in a production environment.
C. Hire a team of qualified analysts to review and label the machines' historical performance data. Train a model based on this manually labeled dataset.
D. Implement 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.

Answer: D

Explanation:
Option A is incorrect because training a time-series model to predict the machines' performance values, and configuring an alert if a machine's actual performance values significantly differ from the predicted performance values, is not the best way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option assumes that the performance values follow a predictable pattern, which may not be the case for complex systems. Moreover, this option does not use any historical incident data, which may contain useful information for identifying failures. Furthermore, this option does not involve any model evaluation or validation, which are essential steps for ensuring the quality and reliability of the model.
Option B is correct because implementing a simple heuristic (e.g., based on z-score) to label the machines' historical performance data, and training a model to predict anomalies based on this labeled dataset, is a reasonable way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option uses a simple and fast method to label the historical performance data, which is necessary for supervised learning. A z-score is a measure of how many standard deviations a value is away from the mean of a distribution1. By using a z-score, we can label the performance values that are unusually high or low as anomalies, which may indicate failures. Then, we can train a model to learn the patterns of normal and anomalous performance values, and use it to predict anomalies on new data. We can also evaluate and validate the model using metrics such as precision, recall, or F1-score, and compare it with other models or methods.
Option C is incorrect because developing a simple heuristic (e.g., based on z-score) to label the machines' historical performance data, and testing this heuristic in a production environment, is not a safe way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option does not involve any model training or evaluation, which are essential steps for ensuring the quality and reliability of the solution. Moreover, this option does not test the heuristic on a separate dataset, such as a validation or test set, before deploying it to production, which may lead to errors or failures in the production environment.
Option D is incorrect because hiring a team of qualified analysts to review and label the machines' historical performance data, and training a model based on this manually labeled dataset, is not a feasible way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option may produce high-quality labels, but it is also costly, time-consuming, and prone to human errors or biases. Moreover, this option may not scale well with large or complex datasets, which may require more analysts or more time to label.
Reference:
Z-score
[Predictive maintenance]
[Anomaly detection]
[Time-series analysis]
[Model evaluation]

NEW QUESTION # 60
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 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
B. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
C. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
D. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage

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 # 61
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
C. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
D. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.

Answer: A

Explanation:
Explanation

NEW QUESTION # 62
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