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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 2: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Topic 3: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 4: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 5: Maintaining and automating data workloads | 18% | - Resource optimization
|
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NEW QUESTION # 414
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
Answer: B
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
NEW QUESTION # 415
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
Answer: A
Explanation:
Explanation
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
Reference: https://en.wikipedia.org/wiki/Apache_Hive
NEW QUESTION # 416
You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely.
The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?
Choose 2 answers
Answer: B,D
Explanation:
To ensure that the data analytics team does not have access to sensitive columns, you should:
* B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags. This role allows users to read metadata for data assets that have policy tags applied, which could include sensitive information.
* C. Enforce access control in the policy tag taxonomy. By setting access control at the policy tag level, you can restrict access to specific columns within a dataset, ensuring that only authorized users can view sensitive data.
NEW QUESTION # 417
You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?
Answer: A
Explanation:
Datastream is a serverless, scalable, and reliable service that enables you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Pub/Sub. Datastream captures and streams database changes using change data capture (CDC) technology. Datastream supports private connectivity to the source and destination systems using VPC networks. Datastream also provides a connection profile to BigQuery, which simplifies the configuration and management of the data replication. Reference:
Datastream overview
Creating a Datastream stream
Using Datastream with BigQuery
NEW QUESTION # 418
Your team runs a complex analytical query daily that processes terabytes of data. Recently, after running for
20 minutes, the query fails with a "Resources exceeded" error. You need to resolve this issue. What should you do?
Answer: C
Explanation:
Comprehensive and Detailed Explanation:The error message "Resources exceeded" in BigQuery indicates that the query's execution plan is too complex or requires more computational resources (slots) than are available to it in the on-demand, fair-share pool.
Option D is the correct answer. BigQuery's on-demand pricing model uses a massive, shared pool of processing units called slots. While this pool is large, a single query cannot monopolize it, and there are limits to prevent runaway jobs. For consistently complex, high-resource queries, the solution is to switch to capacity- based pricing by purchasing slot reservations (e.g., using BigQuery editions). This provides your project with a dedicated, guaranteed amount of processing capacity, ensuring your complex queries have the resources they need to complete successfully.
Option A is incorrect because API request quotas relate to the number of API calls (e.g., how many jobs you can submit per minute), not the computational resources allocated to a single running query.
Option B is incorrect because table size limits are not related to query execution resources.
Option C is incorrect because while a syntax error would cause a query to fail, it would do so immediately with a syntax error message, not after 20 minutes with a "Resources exceeded" error. While optimizing the query is a good practice, the most direct way to solve a resource limit issue is to provide more resources.
Reference (Google Cloud Documentation Concepts):The Google Cloud documentation on "BigQuery pricing" explains the two main models: on-demand pricing and capacity-based pricing (editions). The
"Resources exceeded" error is a known limitation of the on-demand model for extremely demanding queries.
The documentation on "Introduction to slots" and "Reservations" explicitly presents purchasing dedicated slots as the solution for gaining more predictable and higher query performance for demanding workloads.
NEW QUESTION # 419
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