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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 2: Designing data processing systems | 20% | - Designing for business requirements
|
| Topic 3: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 4: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Topic 5: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
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NEW QUESTION # 348
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
Answer: C
Explanation:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotting
NEW QUESTION # 349
Which of the following are feature engineering techniques? (Select 2 answers)
Answer: C,D
Explanation:
Explanation
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model
NEW QUESTION # 350
You currently use a SQL-based tool to visualize your data stored in BigQuery. The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?
Answer: B
NEW QUESTION # 351
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?
Answer: D
Explanation:
To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:
* Materialized Views:
* Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.
* They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.
* Efficiency and Cost Reduction:
* By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.
* Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.
* Minimized Maintenance:
* Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.
Steps to Implement:
* Identify Aggregation Queries:
* Analyze the existing queries to identify common aggregation patterns at the day and month levels.
* Create Materialized Views:
* Create materialized views in BigQuery for the identified aggregation patterns. For example CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS SELECT DATE(transaction_time) AS day, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY day; CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS SELECT EXTRACT(YEAR FROM transaction_time) AS year, EXTRACT(MONTH FROM transaction_time) AS month, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY year, month;
* Query Using Materialized Views:
* Update existing queries to use the materialized views instead of directly querying the base table.
Reference Links:
* BigQuery Materialized Views
* Optimizing Query Performance
NEW QUESTION # 352
How can you get a neural network to learn about relationships between categories in a categorical feature?
Answer: B
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
NEW QUESTION # 353
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